U.S. patent application number 14/172799 was filed with the patent office on 2014-07-31 for enrollment apparatus, system, and method.
This patent application is currently assigned to Proiam, LLC. The applicant listed for this patent is Proiam, LLC. Invention is credited to Will Crosby, Mark Durbin, Eric Metois, Mike Murphy, Wilson Pearce, Paul Yarin.
Application Number | 20140211982 14/172799 |
Document ID | / |
Family ID | 40678989 |
Filed Date | 2014-07-31 |
United States Patent
Application |
20140211982 |
Kind Code |
A1 |
Murphy; Mike ; et
al. |
July 31, 2014 |
ENROLLMENT APPARATUS, SYSTEM, AND METHOD
Abstract
An apparatus for enrolling a package is disclosed including: a
receiving surface for receiving the package; at least one weight
sensor in communication with the receiving surface which generates
a weight signal indicative of the weight of the package; at least
one video camera which generates a video signal indicative of an
image of the package on the receiving surface; and a processor in
communication with the at least one weight sensor and the at least
one video camera. The processor includes: a weight module which
produces, in response to the weight signal, weight data indicative
of the weight of the package; and a dimension capture module which
produces, in response to the video signal, dimension data
indicative of the size of the package. In some embodiments, the
processor further includes a recognition module which produces, in
response to the video signal, character data indicative of one or
more characters present on the package.
Inventors: |
Murphy; Mike; (Salem,
NH) ; Yarin; Paul; (Los Angeles, CA) ; Metois;
Eric; (Arlington, MA) ; Crosby; Will; (Jamaica
Plain, MA) ; Pearce; Wilson; (Orleans, CA) ;
Durbin; Mark; (Banbury, GB) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Proiam, LLC |
Fairfax |
VA |
US |
|
|
Assignee: |
Proiam, LLC
Fairfax
VA
|
Family ID: |
40678989 |
Appl. No.: |
14/172799 |
Filed: |
February 4, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13410877 |
Mar 2, 2012 |
8645216 |
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14172799 |
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12324204 |
Nov 26, 2008 |
8140395 |
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13410877 |
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60990115 |
Nov 26, 2007 |
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61082762 |
Jul 22, 2008 |
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Current U.S.
Class: |
382/101 |
Current CPC
Class: |
G07B 17/00661 20130101;
G06K 9/00671 20130101; G06Q 10/087 20130101; G07B 17/00193
20130101; G06Q 20/202 20130101; G07B 17/00508 20130101; G07B
2017/00701 20130101; G06Q 20/208 20130101; G06K 2209/01 20130101;
G06K 9/00201 20130101; G06K 9/2081 20130101; G07B 2017/00685
20130101; G06T 7/62 20170101; G07B 2017/00225 20130101; G07B
2017/00725 20130101 |
Class at
Publication: |
382/101 |
International
Class: |
G06K 9/00 20060101
G06K009/00; G06T 7/60 20060101 G06T007/60 |
Claims
1-40. (canceled)
41. Non-transitory computer readable media storing computer
executable instructions, which when executed by a processor, cause
the processor to carry out a method comprising: receiving a video
signal of an image of a package on a receiving surface, the video
signal including a stream of two dimensional image frames;
generating from the received video signal tracking data indicative
of a presence and a location of the package on the receiving
surface, wherein the tracking information comprises length and
width dimension data indicative of the size of the package along
two axes lying in a plane parallel to the receiving surface; and
producing dimension data indicative of a size of the package based
on the tracking data.
42. The non-transitory computer readable medium of claim 41,
wherein the method further comprises generating weight data
indicative of the weight of the package.
43. The non-transitory computer readable medium of claim 41,
wherein the method further comprises generating character data
indicative of one or more characters present on the package.
44. The non-transitory computer readable medium of claim 41,
wherein the tracking data further comprises height dimension data
indicative of the size of the package along an axis transverse to
the plane parallel to the receiving surface.
45. The non-transitory computer readable medium of claim 41,
wherein the method further comprises generating height dimension
data in response to receiving a stereoscopic video signal.
46. The non-transitory computer readable medium of claim 41,
wherein the method further comprises generating edge data
indicative of the location of one or more edges of the package.
47. The non-transitory computer readable medium of claim 41,
wherein the method further comprises: generating two dimensional
difference images from two or more two dimensional images of the
video signal; and generating information indicative of the presence
or location of the package based on color information from the
video signal.
48. The non-transitory computer readable medium of claim 41,
wherein the method further comprises: applying a Hough
transformation to one or more images from the video signal; and
analyzing the transformed images to determine information
indicative of the size of location of edges on the package.
49. The non-transitory computer readable medium of claim 41,
wherein the method further comprise: processing one or more two
dimensional images from the video signal; and analyzing the one or
more processed images to produce character data indicative of one
or more characters present on the package.
50. The non-transitory computer readable medium of claim 41,
wherein the method further comprises: modifying the color of the
one or more images; applying a linear image filter to the one or
more images; and applying one or more morphological operation to
the one or more images.
51. The non-transitory computer readable medium of claim 41,
wherein the method further comprises segmenting the one or more
images into one or more regions of interest based on the content of
the images.
52. The non-transitory computer readable medium of claim 41,
wherein the method further comprises rotating at least a portion of
the one or more images.
53. The non-transitory computer readable medium of claim 41,
wherein the method further comprises combining overlapping two
dimensional images from at least two cameras to produce a single
image of a combined field of view of the cameras.
54. The non-transitory computer readable medium of claim 41,
wherein the processor is communicatively coupled to at least a
weight sensor and a video camera unit.
55. The non-transitory computer readable medium of claim 54,
wherein the weight sensor generates a weight signal indicative of a
weight of the package.
56. The non-transitory computer readable medium of claim 55,
wherein the weight sensor comprises at least one of: a load cell, a
MEMs device, a piezoelectric device, a spring scale, and a balance
scale.
57. The non-transitory computer readable medium of claim 54,
wherein the video camera unit generates the video signal indicative
of the image of the package on the receiving surface, wherein the
video signal comprises a stream of two dimensional image
frames.
58. The non-transitory computer readable medium of claim 57,
wherein the video camera unit comprises a stereoscopic imager.
59. The non-transitory computer readable medium of claim 57,
wherein the video camera unit comprises at least two cameras, each
camera unit generating a respective video signal, and wherein the
cameras have at least partially overlapping fields of view.
60. The non-transitory computer readable medium of claim 57,
wherein the video camera unit comprises an infrared camera.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application claims benefit of U.S. Provisional
Application Ser. No. 60/990,115 filed Nov. 26, 2007 and U.S.
Provisional Application Ser. No. 61/082,762 filed Jul. 22, 2008,
the contents of each of which are incorporated herein by reference
in their entirety.
BACKGROUND
[0002] In industries such as the postal, courier, and supply chain
industries, package objects (e.g. letters or parcels) are enrolled
into a system for tracking and/or delivery. For example, items
presented at post offices and related locations for onward delivery
currently require the counter staff and/or customers to manually
enter data regarding address information, weight, dimensional
information, and other shipment characteristics. The manual
collection of this information is costly, in terms of time, error
rates on manual data entry, plus errors in correctly rating items.
Retail locations are also typically required to maintain space for
both the scale and the metering devices.
[0003] Some postal authorities mandate the capture of additional
information for items being accepted across post office counters.
It is now a common requirement that dimensions, destination, and
sender information be captured. Complexity is increasing while the
ability to maintain a well trained counter staff is declining.
Increasing use of franchise points of presence is causing
compliance, training, accounting, and security problems.
[0004] For example, a customer may bring a package to a post office
point of sale. A postal employee will receive the package, gather
information related to the package (e.g. intended destination,
package dimensions, package weight, type of delivery, desired
delivery date, customer information, payment information etc.). In
typical situations, the information is gathered manually, and in a
highly linear and labor intensive fashion. For example, in a
typical transaction, a postal employee might receive a package,
weigh it, measure, its dimensions with a tape measure, enter this
information into a computer, read a label on the package, enter
delivery address information found on this label, query the
customer about desired delivery type and date, enter this
information into a computer, provide pricing information to the
customer, accept payment, etc.
[0005] Improvement in enrollment efficiency could provide
substantial savings in, for example, labor costs, error costs, and
time.
SUMMARY
[0006] The inventors have realized that enrollment efficiency can
be increased by automatically, and substantially simultaneously
collecting multiple types of information about an item being
enrolled in a delivery system.
[0007] In one aspect, an enrollment device is disclosed which will
replace both the traditional weigh scale, as well as the postage
meter, which are currently found at induction points for Postal,
Courier and Supply Chain operations. A combination of Optical
Character Recognition (OCR) and dimension capture (e.g. using
optical dimension capture and/or ultrasonic range-finding
technologies) is used to capture and convert addressing, payment,
account and shipment related data, plus weight and dimensional
information (when relevant) from packages, letters, and
documentation which are placed on, in, or near the device.
[0008] Such a device provides a "front end" mechanism for entering
shipment related data into a business environment (e. g. postal
environment) and simultaneously automates the rating and data
collection process for accepting goods and services, automates the
process of capturing dimensional data in the course of rating
shipments at point of induction into the business environment,
reduces or eliminates the requirement for a separate weigh scale,
reduces or eliminates the requirement for a separate metering
device, and presents data to the organization's back-end and
enterprise systems at point of induction.
[0009] In one aspect, an apparatus for enrolling a package is
disclosed including: a receiving surface for receiving the package;
at least one weight sensor in communication with the receiving
surface which generates a weight signal indicative of the weight of
the package; at least one video camera which generates a video
signal indicative of an image of the package on the receiving
surface; and a processor in communication with the at least one
weight sensor and the at least one video camera. The processor
includes: a weight module which produces, in response to the weight
signal, weight data indicative of the weight of the package; and a
dimension capture module which produces, in response to the video
signal, dimension data indicative of the size of the package. In
some embodiments, the processor further includes a recognition
module which produces, in response to the video signal, character
data indicative of one or more characters present on the
package.
[0010] Some embodiments include a range finder sensor in
communication with the processor which produces a range finder
signal indicative of the size of the package, and where the
dimension capture module produces, in response to the video signal
and the range finder signal, dimension data indicative of the size
of the package. In some embodiments, the dimension capture module
produces, in response to the video signal, dimension data
indicative of the size of the package along two axes lying in a
plane substantially parallel to the receiving surface, and
produces, in response to the range finder signal, dimension data
indicative of the size of the package along an axis transverse to
the plane.
[0011] In some embodiments, the dimension capture module includes a
tracking module which produces, in response to the video signal,
tracking data indicative of the presence and location of the
package on the receiving surface.
[0012] In some embodiments, the at least one camera selectively
operates in a first mode characterized by a relatively large field
of view to generate a video signal characterized by a relatively
low resolution and relatively high frame rate, and a second mode
characterized by a relatively small field of view to generate a
video signal characterized by a relatively high resolution and a
relatively low frame rate. In some embodiments, the tracking module
produces, in response to the video signal generated by the at least
one camera in the first mode, tracking data indicative of the
presence and location of the package on the receiving surface. The
recognition module produces, in response to the video signal
generated by the at least one camera in the second mode, character
data indicative of one or more characters present on the
package.
[0013] In some embodiments, the dimension capture module includes
an edge finder module which produces, in response to the video
signal, edge data indicative of the location of one or more edges
on the package.
[0014] In some embodiments, the dimension capture module includes
one or more of: a frame differencing module which generates
difference images from two or more images of the video signal; and
a color masking module configured to generate information
indicative of the presence or location of the package based on
color information from the video signal.
[0015] In some embodiments, the dimension capture module includes a
Hough transformation module which applies the Hough transformation
to one or more images from the video signal, and analyzes the
transformed images to determine information indicative of the size
or location of edges on the package. Some embodiments further
include a rectangle tracking module configured to track the
location of a substantially rectangular package based on the
information indicative of the size or location of edges on the
package.
[0016] In some embodiments, the recognition module includes: an
image processing module for processing one or more images from the
video signal; and an analysis module which analyzes the one or more
processed images to produce the character data indicative of one or
more characters present on the package.
[0017] In some embodiments, the image processing module includes at
least one of: a color manipulating module for modifying the color
of the one or more images; a linear filter module for applying a
linear image filter to the one or more images; and a morphological
filter module for applying one or more morphological operations to
the one or more images.
[0018] In some embodiments, the image processing module includes an
image segmentation module for segmenting the one or more images
into one or more regions of interest based on the content of the
images.
[0019] In some embodiments, the image processing module includes an
image rotation module which rotates at least a portion of the one
or more images. In some embodiments, the image rotation module is
in communication with the dimension capture module, and rotates at
least a portion of the one or more images based on information from
the dimension capture module indicative of the location of the
package.
[0020] In some embodiments, the at least one camera include at
least two cameras, each generating a respective video signal, and
where the cameras have least partially overlapping fields of view.
The processor includes an image stitching module which, in response
to the respective video signals, combines overlapping images from
the at least two cameras to produce a single image of the combined
field of view of the cameras.
[0021] In some embodiments, each of the at least one cameras has a
respective field of view. Substantially all locations on the
receiving surface fall within the respective field of view. In some
embodiments, the at least one camera consists of a single
camera.
[0022] In some embodiments, the receiving surface is at least
partially transparent, and where the at least one camera is
positioned below the receiving surface to image the package through
the receiving surface.
[0023] In some embodiments, the at least one camera is positioned
above the receiving surface.
[0024] In some embodiments, the receiving surface includes at least
on indicia for aligning or focusing the at least one camera.
[0025] In some embodiments, the at least one camera includes an
autofocus.
[0026] Some embodiments include an integrated housing including the
receiving surface and processor.
[0027] Some embodiments include an arm extending between a proximal
end connected to the housing and a distal end positioned above the
receiving surface, the distal end including at least one chosen
from the group consisting of: a range finder, and the at least one
camera.
[0028] Some embodiments include an RFID reader, and/or a bar code
reader.
[0029] In some embodiments, the rangefinder includes at least one
of: an ultrasonic range finder, a RADAR range finder, a LIDAR range
finder, a laser range finder, an LED based range finder, a
mechanical range finder, and an optical range finder.
[0030] In some embodiments, the at least one weight sensor
includes: a load cell, a MEMs device, a piezoelectric device, a
spring scale, and a balance scale.
[0031] In some embodiments, the data indicative of one or more
characters present on the package include data indicative of at
least one chosen from the group consisting of: an alphanumeric
character, a symbol, a postal code, a post mark, a bar code, and a
two dimensional bar code.
[0032] Some embodiments include a postal meter in communication
with the processor and/or a printer in communication with the
processor.
[0033] In another aspect, a method of enrolling a package is
disclosed including: providing an enrollment apparatus of the type
described herein, using the enrollment apparatus to determine
information indicative of the size of the package; using the
enrollment apparatus to determine information indicative of the
size of the package indicative of one or more characters present on
the package; and outputting the information indicative of the
weight, size, and one or more characters present on the
package.
[0034] In some embodiments, the information indicative of the
weight, size, and one or more characters present on the package,
respectively, are determined substantially in parallel.
[0035] In another aspect, a system is disclosed including: an
enrollment apparatus of the type described herein and a package
management system. The enrollment apparatus is in communication
with the package management system to provide information
indicative of the weight, size, and one or more characters present
on the package.
[0036] In some embodiments, the package management system includes
at least one of: a package delivery system, a supply chain
management system, an inventory management system, and a chain of
custody management system.
[0037] In some embodiments, the package management system includes
a point of sale unit, and where the point of sale unit generates
and displays, in response to the information indicative of the
weight, size, and one or more characters present on the package,
information indicative of one or more available service options. In
some embodiments, the point of sale unit includes an input unit for
receiving information from a user; and a service unit for providing
information indicative of one or more available service options
based on the information from the user and information indicative
of the weight, size, and one or more characters present on the
package. In some embodiments, where the input unit includes the
enrollment apparatus.
[0038] In some embodiments, the input unit receives a series of
instructions from the user, and the service unit includes a
backwards chaining logic unit which dynamically determines and
displays available service options based on the series of
instructions and based information indicative of the weight, size,
and one or more characters present on the package.
[0039] In some embodiments, the package management system includes
a point of service unit, and further including a handler module for
facilitating communication between the enrollment apparatus and the
point of service unit.
[0040] As used herein, the "location" of a package refers to its
position in space and its orientation.
[0041] Various embodiments may include any of the features
described above, either alone or in any combination.
[0042] The details of one or more embodiments are set forth in the
accompanying drawings and the description below. Other features and
advantages will be apparent from the description and drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0043] FIGS. 1a-1c show views of an enrollment device.
[0044] FIG. 2 shows and illustration of the connections and control
of the various components of an enrollment device.
[0045] FIG. 3 shows exemplary specifications for an enrollment
device.
[0046] FIGS. 4a-4e are photographs of a working example of an
enrollment device.
[0047] FIGS. 5, 6a and 6b show views of alternate embodiments of an
enrollment device.
[0048] FIG. 7 is a flow diagram illustrating operation of an
enrollment device.
[0049] FIG. 8 is a diagram of an exemplary processor.
[0050] FIG. 9 is an illustration of a system featuring an
enrollment device.
[0051] FIG. 10 illustrates modules included in an enrollment
device.
[0052] FIG. 11 illustrates image processing by an enrollment
device.
[0053] FIG. 12 illustrates a Hough transform.
[0054] FIG. 13 illustrates segmented address information.
[0055] FIGS. 14, 14a-14b, 15a-15c, and 16 illustrate graphical user
interface screens for an enrollment device.
DETAILED DESCRIPTION
[0056] FIGS. 1a, 1b, and 1c illustrate an exemplary embodiment of
an enrollment device 100. Referring to the cutaway view of FIG. 1a,
the device body 102 (also referred to herein as "main enclosure")
includes a transparent tempered glass surface 104 for receiving a
package 106 (shown in FIGS. 1b and 1c). Load cells 108 (e.g. solid
state load cells) are located at the corners of the glass surface
and provide weight information for items placed on the surface
104.
[0057] The device body 102 includes two cameras 110. First and
second surface mirrors 112 are disposed to direct an image of a
package placed on the surface to the cameras. The marginal rays of
the camera/mirror systems are indicated in FIG. 1a. As shown, the
combined field of view of the two cameras 110 substantially covers
the area of the glass surface 104, allowing image capture of
package 106 placed at an arbitrary position on the glass surface
104.
[0058] The device body also includes a computer processor 114 which
may be coupled to the various components of the device and/or to
external systems or devices. For example, the computer processor
114 may be an x86 platform capable of running Linux or embedded
Microsoft Windows products. In various embodiments, this computer
may run the internal "firmware" for the device as well as support
application facilities such as a Web Server and postal rating (i.e.
pricing/metering) engine.
[0059] In some embodiments, the device body 102 includes one or
more lighting modules (not shown), such as light emitting diode
modules, to illuminate the package placed on the glass surface.
[0060] A support arm 116 (also referred to herein as an "extension
arm") extends above the surface 104. The support arm 116 includes
control buttons 118 (e.g. power control, measurement units control,
scale tare, etc.). A display 120 provides information to the user
or users (e.g. postal clerk and/or customer) and may include for
example, a character display (e.g. LCD display). The support arm
116 also includes an ultrasonic transducer rangefinder 122 which
operates to capture one or more dimensions of package 106 placed on
the glass surface 104 (e.g. the height dimension as shown in FIGS.
1b and 1c). In some embodiments, the device 100 may include
additional or alternative rangefinders (e.g. infrared rangefinder,
mechanical rangefinder, laser rangefinder, radar range finder, LED
based rangefinder, one or more cameras, etc.)
[0061] FIG. 2 illustrates the connections and control of the
various components of an enrollment device of the type described
above. Compact personal computer (PC) 202 (e.g. comprising
processor 114) is connected to a microcontroller 204. The
microcontroller receives analog inputs from four load cells 206 and
an infrared rangefinder 208, along with digital inputs from an
ultrasonic rangefinder 210 and user control buttons 212.
Information from these inputs can be passed back to the compact PC
202 for processing. The microcontroller 204 also provides digital
control outputs to a display 214, LED indicators 216, and a beeper
218. The compact PC 202 receives image information from each of two
cameras 220 for processing (e.g. image processing, OCR, dimension
capture, etc). The compact PC 202 is further connected to various
peripherals 221 via a connection such as a universal serial bus
(USB) hub 222. The peripherals may include a printer, an RFID
reader capable of receiving signals from an RFID tag on the
package, and various displays and controllers (e.g. keyboard, touch
screen display, touchpad, etc.).
[0062] As will be understood by one skilled in the art, FIG. 3
lists various parameters and specifications for a working example
of an enrollment device of the type described above, along with
target performance specifications corresponding to typical
applications. Note that the majority of performance characteristics
of the working example are in general compliance with target
values.
[0063] FIGS. 4a-4e are photographs of a working example of an
enrollment device of the type described above. FIG. 4a shows the
device with a package placed on the glass surface. FIG. 4b shows
the device along with display and control peripherals. FIG. 4c
shows a compact PC integrated into the main enclosure. FIGS. 4d and
4e show examples of image processing, dimension capture, and OCR,
as will be discussed in greater detail below.
[0064] Although an exemplary embodiment is presented above, it is
to be understood that other suitable configurations for the
enrollment device may be used. For example, FIG. 5 shows a
perspective view of an exemplary embodiment of an enrollment device
100. In this configuration, instruments such as an ultrasonic
rangefinder 122 and/or RFID reader are incorporated in a spherical
enclosure 502 on top of an extension arm positioned at the corner
of the device's main enclosure 102. Control buttons 118 and an
organic LED (OLED) display 120 are positioned on the main enclosure
102.
[0065] FIG. 6a shows another exemplary embodiment, in which cameras
110 are placed on the extension arm 116 instead of in a main
enclosure of the device, thereby providing a top down view of a
package placed on the surface 104 of a weight scale 601. In some
applications, this configuration may provide additional comfort for
users accustomed to placing packages with labels or other printed
information "face up", while still allowing for dimension capture,
OCR, etc. As shown, processor 114 is located externally, but in
other embodiments it may be located integrally.
[0066] FIG. 6b shows a similar embodiment featuring a single camera
110. Camera 110 may have a field of view larger than and
encompassing surface 104, such that even packages which are as
large or larger than package receiving surface 104 of weight scale
601 may be imaged. Camera 110 may include an autofocus or other
focusing and/or alignment systems. Indicia 602 on surface 104 may
be used to aid in focusing and/or alignment of camera 110.
[0067] FIG. 7 illustrates the flow of an enrollment process 700
using a device 100 of the type described above. Initially, in step
701 the package to be enrolled is received on the receiving surface
104 of the enrollment device 100. In step 702, the presence of the
package is detected, for example, as described in greater detail
below, by processing a stream of video images captured by the
cameras (or camera) 110.
[0068] Once the presence of the package is detected, multiple types
of information about the package are captured in parallel steps. In
step 702, the weight of the object is captured, e.g. by the load
cells 198 or scale 601.
[0069] In step 702, the cameras 110 capture one or more images of
the package. The images undergo a processing step 703 to provide
information about the package. For example, in step 705 machine
vision applications (e.g. edge detection) may be used to capture
one or more dimensions (e.g. length, width) of the package. Optical
character recognition techniques can be used in step 704 to capture
text or other markings on the package (e.g., postal
markings/permits, bar codes, etc.).
[0070] In step 706, one or more dimensions of the package are
captured. For example, the height of the package may be determined
by the ultrasonic range finder 122. This information can be
combined with dimension information determined in the image
processing step to provide complete dimensional information (e.g.
length, width and height) of the package.
[0071] In step 707, the enrollment device 100 captures other types
of information related to the package. For example, an RFID reader
connected to or integrated with the enrollment device can gather
information from an RFID tag on the package.
[0072] In step 708, the information captured in the above described
steps is then collected, processed, and/or stored. The information
may also be output, for example to a delivery service business
system. The information may be output in any suitable form
including electronic data, an analog signal, printed material,
visual display, etc.
[0073] For example, in some embodiments, information is displayed
to a user via a graphical user interface. The user may confirm or
edit the captured information, enter additional information, query
a customer as to a choice of delivery options or additional
services, etc. In some embodiments, printed material (e.g. labels,
stamps, etc.) may be output from an attached or integral printer.
In some embodiments, output can include markings (e.g. barcodes)
printed directly onto the package using, for example, an attached
or integral spray printing system, or through attaching separately
printed labels with bar code, postage, or related package
information--based on information derived from the device.
[0074] In some embodiments, the performance of one or more steps
might depend on the results of other steps. For example, the
imaging and OCR of a package might determine that the package was a
"flat rate" envelope of the type common in postal and delivery
services. In such a case, weight and dimensional information is not
relevant, and thus the steps used to capture this type of
information may be omitted.
[0075] FIG. 8a shows an exemplary embodiment of processor 114.
Video signals from cameras 110 are input to frame stitching module
801 which combines multiple overlapping views of surface 104 into a
single view (in embodiments featuring a single camera may omit this
module). The combined video signal is passed to dimension capture
module 802 and recognition module 803. Rangefinder signal may also
be passed from rangefinder 122 to dimension capture module 802 and
recognition module 803. Using, e.g. the techniques described
herein, dimension capture module 802 operates to produce dimension
data indicative of the size (e.g. length, width, and/or height) of
a package based on the input signals. For example, module 802 may
determine the length and width of the object based on edge finding
processing of the combined video signal and the height of the
package based on the rangefinder signal.
[0076] Using, e.g. the techniques described herein, recognition
module 804 operates to produce character data related to one or
more characters (e.g. alphanumeric address, bar code, postal mark,
symbols, etc) found on the package.
[0077] Weight module 804 receives a weight signal input from a
weight sensor such as load cells 122 or scale 601, and produces
weight data indicative of the weight of a package placed on surface
104.
[0078] Processor 114 combines the weight, dimension, and character
data from modules 802, 803, and 804 and outputs the data from
output 805. The operation of the modules described above will be
further described below.
[0079] FIG. 9 illustrates the integration of an enrollment device
100 into an exemplary delivery system 900. As described above, an
enrollment device 100 (captures numerous pieces of information
which are passed on to and processed by processor 114 (e.g. via
firmware run by a compact PC integrated with or linked to device
100). Processor 114 may communicate (e.g. using a network
connection), with one or more servers 901. For example, an address
management server could exchange information related to redirection
or alternate delivery. A rights management server could exchange
information to validate permits or confirm postage. A supervised
delivery server could exchange information related to package
tracking or chain of custody (e.g. for prescription medications or
legal evidence). In some embodiments, these servers might further
interact with other "back end" applications including supervised
delivery application 902 and database management applications 903.
Such applications could be connected via a network 904 (e.g., an
intranet, extranet, the world wide web, etc.)
[0080] Processor 114 interacts with a point of service (POS) system
905 (e.g. a postal service counter sales system) to provide, for
example, validated address or redirection information, weight,
dimensions, etc. Interactions might be mediated by an event handler
application 906 which interrupts or otherwise communicates with the
POS system to provide, for example, invalid permit, address, or
delivery point warnings, redirection information, scale/OCR timeout
indications, etc.
Enrollment Functions
[0081] The following describes more detailed examples of the
various functions which may be carried out by enrollment device
100.
Scale Function
[0082] In some embodiments, the enrollment device 100 includes a
scale 601 for acquiring information about the weight of a package.
For example, in various embodiments, a solid state weighing device
(e.g. including one or more load cells 118) operates with
accuracies consistent with relevant standards (e.g. US Postal
Service and/or Royal Mail requirements). Direct management of a
display device may be provided in support of weights and measure
requirement.
[0083] In some embodiments, detailed usage history is kept in order
to ensure accurate performance throughout the life of the scale.
Remote supervision may be provided (e.g. via an internet connection
provided through an integrated compact PC). Suspect scales can be
identified via an analytics application.
Imaging Function
[0084] In typical applications, the enrollment device 100 detects
the presence of a package and captures an image of at least a
portion of the package. The image is processed to derive
information from the package (e.g. from mailing labels or printed
markings) including: printed address/destination info, sending
identification information, postal markings, and other information
such as proprietary barcode information. In various embodiments the
enrollment device acquires this information in an automated
fashion, performed in such a way as to have reduced negative impact
on currently sorting.
[0085] Referring to FIG. 10, in some embodiments, the image related
tasks of the enrollment device are performed by four modules: the
imaging device module 1001, the tracking module, the image
enhancement and dimension capture module 1003 and the recognition
module 1004. All or portions of the above modules may be included
in processor 114.
[0086] The imaging device module 1001 employs one or more cameras
110 to obtain images of a package. The imaging device module 1001
may operate to meet two different sets of requirements imposed by
the tracking module 1002 and the recognition module 1004. As will
be described below, mail piece tracking module 1002 typically
requires image capture with a relatively large field of view and a
relatively high frame rate, but can tolerate relatively low
resolution. The recognition module 1004, on the other hand,
requires relatively high resolution images (e.g. about 200 dots per
inch, "dpi"), but can typically tolerate a relatively narrow field
of view and relatively slower frame rate. Accordingly, in some
embodiments, the imaging device module 1001 operates in a first
mode to provide a low resolution but large field of view (e.g.
substantially covering the surface 104 of a device 100) and high
frame rate image stream to the tracking module 1002. When a package
is placed on receiving surface 104 of the enrollment device 100,
the tracking module identifies the package's presence, location
(i.e. position and/or orientation), and size. The imaging module
1001, using information from the tracking module 1002, then
switches to a high resolution mode to capture high quality images
of areas of interest (e.g. an area including an address label) on
the package.
[0087] Note that in various embodiments these modules may be
implemented in hardware (e.g. using multiple cameras or sensors of
varying resolution) or in software (e.g. using image processing
techniques known in the art) or in a combination thereof.
[0088] As mentioned above, the tracking module 1002 operates to
monitor a stream of image information from the imaging device
module 1001 to detect the presence of and determine the size and
location/orientation of a package placed on receiving surface 104
of the enrollment device 100. Several tracking techniques will be
described herein, however, it is to be understood that the tracking
function may be performed by any suitable techniques (e.g. using
known machine vision applications).
[0089] In some embodiments, the tracking module 1002 employs a
color masking module 1005. Color masking is a technique used when
looking for an object which leverages unique color information that
the object might have (e.g., brow coloring for parcels) and/or that
the background may have (e.g. the known color of surface 104). In
typical applications, the color masking process consists of
removing any pixel of an image that deviates to a specific range of
color values.
[0090] For this type of approach, the well known RGB color space is
sometimes not the most appropriate if one wants to avoid artifacts
due to lighting inconsistencies. Instead, computing color
deviations in the YUV or the YCbCr color spaces typically leads to
better results. For reference, Y is usually referred to a luminance
and turning an RGB color value in the YCbCr color space can be done
through these simple relationships:
Y=0.31R+0.59G+0.11B;Cr=R-Y;Cb=B-Y
The advantage of this color representation is that lighting
inconsistencies will typically incur radial shifts of the (Cb, Cr)
value around the center of this plane. Hence the angle of a polar
representation of this color plane can be fairly invariant through
lighting changes. It is also noteworthy to notice that this angle
is closely related to the concept of a color's hue.
[0091] In some embodiments, the tracking module 1002 employs motion
analysis using, for example, frame differencing module 1006. For
example, one way to detect motion is through a frame differencing
process. As the system (e.g. featuring a stationary camera) gathers
successive video frames it simply compares each pixel value to its
value in the previous frames and removes those that have not
changed significantly. When the images are provided as grayscale,
intensity is the only available parameter but in the case of color
images there are alternative ways to perform these differences
depending on the color space.
[0092] Such a frame differencing process is effectively a temporal
high-pass filter and as such it is highly prone to pixel noise.
Therefore it is often coupled with subsequent image processing
stages such as linear or morphologic filters, which are discussed
below.
[0093] FIG. 11 shows an example of frame difference tracking. A
short series of video frames 1101 were captured of an envelope
being handled in a "visually busy" environment. These frames were
further imported within the Matlab environment where the
differences between successive frames were computed. These
difference images 1102, illustrated in the second row of FIG. 10b,
reveal the mail piece. However, the frame differencing also reveals
any other moving object, such as the person's hand and arm.
[0094] In order to identify a rectangular object (e.g. a package or
envelope) in the frame differences, in some embodiments, the
tracking module 1002 employs the Hough transform module 1007 to
transform the frame differenced data 1102 to produce Hough domain
images 1103. The primary purpose of this transform is to extract
linear graphic elements (i.e. straight lines) from an image. It
effectively does so by maintaining a series of accumulators that
keep track of all lines that pass through a set of points. As many
of these points are collinear, the largest of these accumulators
reveal the equation of that line in the Hough domain. In that
domain, the y-axis corresponds to the orientation of that line and
the x-axis corresponds to the distance between that line and an
origin one chooses in the image. This mapping is shown in FIG. 12.
For example, FIG. 12 shows three points in the spatial domain. For
each one of these points, all the lines that pass through it are
represented by a "vertical sinusoid" in the Hough domain. Because
these three points where chosen to be collinear, notice that the
three corresponding sinusoids intersect. The coordinates (.theta.,
.rho.) of this intersection describe the line that passes through
all three points uniquely.
[0095] Referring back to FIG. 11, the third row of Hough domain
images 1103 shows the Hough domain that corresponds to each frame
difference 1102. As the motion of the mail piece slows down (i.e.
third column in the FIG. 11) and the difference frame starts to
show a clear rectangular outline of the mail piece.
[0096] Note, as shown in the inset of FIG. 11, that the Hough
domain sharpens up, revealing two noticeable peaks lined up
horizontally. The fact that these peaks live on the same horizon in
the Hough domain reveals that these two corresponding lines are
parallel: one has thus found the upper and lower edges of the mail
piece.
[0097] If one were to further look for linear feature that are
perpendicular to these edges one would simply look for local
maximums in the Hough domain at the horizon corresponding to a 90
degrees rotation. In the case of the current example this would
further reveal an estimation of the left and right edges of the
mail piece.
[0098] Rectangle tracking module 1008 can leverage information of
the type described above to track the location/orientation of
rectangular packages. Frame differencing and a Hough transform
provide a solid basis for the tracking of a moving rectangular
object. It has the great benefits of further providing orientation
estimation for the mail piece in the same process, while requiring
no further assumption concerning the size or even the aspect ratio
of the rectangular object.
[0099] In typical applications, color masking and motion analysis
can reveal "blobs" (connected regions) of pixels that maybe of
interest. In some cases this might be not enough to locate the
target or an area of interest. As previously noted, shape-related
image analysis techniques such as the Hough transformation can
provide additional information. Some techniques useful for tracking
include, for example blob segmentation clustering. One useful step
is to group pixels that may belong to the same spatial blob. These
techniques are discussed further in the context of image
enhancement and OCR below.
[0100] One way to quantify a blob of pixels is by measuring its
spatial moments. The first order moment is simply the blob's center
of mass. Its second order moments provide measures about how
"spread" the blob is around its center of mass. Through a simple
diagonalization process these second order moments can further lead
to the blob's principal components, which provide a general measure
of the object's aspect ratio and its orientation. In a 1962
publication, Ming-Kuei Hu suggested a means to normalize and
combine the second and third central moments of a graphical object,
leading to a set of 7 descriptors that have since been referred to
as the Hu-moments. These 7 features have the highly desirable
properties of being translation, rotation and scale invariant. A
number of OCR engines have subsequently been developed based on
these features.
[0101] Extracting the edges of a visual object is also a very
common step that may come handy as one searches for a target mail
piece. One of the most popular methods is the Canny edge detection
algorithm. It is equivalent to the location of local maximums in
the output of a high frequency (gradient) filter. The method
actually starts with the application of a low-pass filter in order
to reduce noise in the image so the whole process can be seen as
some band-pass filtering stage followed by a morphologic processing
stage.
[0102] Once a package presence has been detected and location,
orientation, and size determined by the tracking module 1002, one
or more images of the package at a desired resolution are obtained
by the imaging device module and passed on to the image enhancement
module 1003. In various embodiments, this module operates to
process these images to compensate for the amount of rotation from
ideal registration (i.e. registration with the edges of the surface
104 of the enrollment device 100) that was detected by the mail
piece tracking module. As is known in the art, this can be achieved
through, for example, a resampling stage. In typical applications,
this resampling stage does not require any more than a bilinear
interpolation between pixels.
[0103] As required by the application or environment at hand, some
embodiments employ other image enhancement processing techniques to
provide a high quality image to the recognition module 1004 for,
for example, accurate OCR.
[0104] Depending on the OCR performance achieved, a further
segmentation module 1009 may be added to the image enhancements
module. The typical image analysis technique will make a certain
number of assumptions concerning the input image. Some of these
assumptions might be reasonable in the context of the application
and some others might require a little bit of work on the input.
This is where preprocessing typically comes into play. As a general
rule, the object of a preprocessing stage is to emphasize or reveal
salient features of an image while damping irrelevant or
undesirable ones before attempting to perform further analysis of
the image's content. There are numerous types of processing known
in the art that may share such an objective. Some such processing
types are composed of elementary stages that fall within one of the
following major categories: color manipulations, linear filters,
morphological image processing, or image segmentation.
[0105] Color manipulations include grayscale conversion from a
color image, color depth reduction, thresholding (to a binary image
for instance), brightness and contrast modifications, color
clipping, negation and many others. In such processes, the color
value of an output pixel is a direct function of the input color
value of that same pixel and some global parameters. In some cases,
these global parameters might be derived from an overall analysis
of the input image but once chosen they remain the same during the
processing of all pixels in the image.
[0106] Linear image filters can typically be seen as a convolution
between the input image and another (usually smaller) image that's
sometime referred to as a kernel. Their objective is to reveal
certain spatial frequency components of the image while damping
others. The most commonly used linear filters are either blurring
(low-pass) or sharpening (high-pass) the image. Gradients and
differentiators used for edge detection are another commonly used
type of high-pass linear filters. Performing a brute force 2D
convolution can be a computationally expensive proposition. Indeed
if the filter kernel M is a square image counting N rows and N
columns, processing a single input pixel through the kernel will
require N.sup.2 operations. One way to overcome this prohibitive
scaling is to use what are sometimes referred to as separable
filters. Those are filters for which the kernel M is an
outer-product of two vectors: i.e. M=UV.sup.T, where U and V are
vectors of length N.
[0107] With such a choice for the filter, the sliding correlation
with the matrix M over the entire image can be expressed as the
cascade of two 1D filtering stages over the two dimensions
(horizontal and vertical) of the image. The elements of the vector
V are the impulse response of the 1D filtering stage we first apply
to each row and the elements of the vector U are the impulse
response of the 1D filtering stage we subsequently apply to each
column. Each 1 D filtering stage involves N operations per pixel
and therefore, the entire sliding correlation with the matrix M
involves only 2N operations (as opposed to N.sup.2 if the filter
were not separable).
[0108] The most common separable filters are Gaussian low-pass
filters. The separability of their kernel falls out from the fact
that the product of two Gaussians is also a Gaussian. Note that the
same technique can be applied for separable kernels that are not
square (i.e. the vectors U and V have different lengths). In cases
where the kernel in not separable, one may use techniques known in
the art to approximate the kernel as a combination of separable
filtering stages. These techniques will typically perform an
eigenvalue decomposition of the kernel.
[0109] Other noteworthy special cases of separable linear filters
are filters for which the kernel matrix is filled with the same
value. These are effectively low pass filters that average all
pixel values over a rectangular neighborhood centered on the pixel
position. Although they might exhibit less than ideal frequency
responses they have the great advantage of being computationally
cheap. Indeed regardless of the kernel size, their computation
consists of simple running sums performed subsequently over the
horizontal and vertical direction of the image, requiring a total
of only 4 operations per pixel.
[0110] Morphological image processing is a type of processing in
which the spatial form or structure of objects within an image are
modified. Dilation (objects grow uniformly), erosion (objects
shrink uniformly) and skeletonization (objects are reduced to
"stick figures") are three fundamental morphological operations.
Typically, these operations are performed over binary images for
which there is a clear concept of presence and absence of an object
at every pixel position but these concepts have also been extended
to grayscale images.
[0111] Binary image morphological operations are based on the
concept of connectivity between pixels of the same class. From an
implementation point of view, these operations typically consist of
a few iterations through a set of hit or miss transformations. A
hit or miss transformation is effectively a binary pattern lookup
table. While a linear filter would apply a fixed linear combination
of the input in order to set the output value of a pixel, this
process will set a pixel to either 1 or 0 depending on whether its
surrounding pattern is found in the table or not (Hence the terms
"hit or miss"). Depending on the lookup table, this can effectively
implement a highly non-linear operation.
[0112] Image segmentation includes the division of an image into
regions (or blobs) of similar attributes. As discussed below, an
OCR system will typically include at least one image segmentation
stage. In fact, many suitable image analysis algorithms aiming to
localize, identify or recognize graphical elements perform some
form of image segmentation.
[0113] In general terms this process may consists of a clustering
or classification of pixel positions based on a local graphical
measure. This graphical measure is the image attribute that should
be fairly uniform over a region. In other words, the resulting
regions or blobs should be homogeneous with respect to some local
image characteristic. This local measure may simply consist of the
pixel's color but some applications may require more sophisticated
measures of the image's local texture around that pixel position.
It is also generally understood that a segmentation process should
aim to reveal regions or blobs that exhibit rather simple interiors
without too many small holes.
[0114] The nature of the chosen graphical attribute depends
entirely on the application and the type of blobs one is trying to
isolate. For example, segmenting an image into text versus non-text
regions will require some sort of texture attribute while
segmenting light versus dark areas will only require color
intensity as an attribute.
[0115] Once the chosen attribute has been computed throughout the
image, the remainder of the segmentation process will typically use
an ad-hoc algorithm. One of the most intuitive techniques is
sometimes referred to a region growing and its recursive nature is
very similar in spirit to a floodfill algorithm. More sophisticated
techniques implement clustering processes using classical iterative
algorithms known in the art such as k-means or ISODATA.
[0116] In some applications, it may be necessary to increase the
resolution of the captured image or images. In some embodiments,
resolution of the image may be increased using a technique know as
superresolution. The Nyquist sampling criterion requires that the
sampling frequency should be at least double for the highest
frequency of the signal or image features one wishes to resolve.
For a given image module 1001 focal length, this typically implies
that the smallest optical feature one can resolve will never be
smaller then 2 pixels-worth of a pixilated sensor's (e.g. CCD's)
resolution.
[0117] A common practice to overcome this theoretical limit is to
combine multiple captures of the same object from slightly
different perspectives. While each capture suffers from Nyquist's
limit they form, together, a non-uniform but higher frequency
sampling of the object. The key to this process is the ability to
align these multiple captures with sub-sample accuracy. Once the
individual captures are up-sampled and aligned, they can carefully
averaged based on their sampling phase. This process effectively
re-constructs a capture of the object with higher sampling
frequency, and hence a higher image resolution. Variations of such
techniques are known from, for example, the field of image
processing.
[0118] Once an image has been processed by the image enhancement
module 1003, it is passed on to the recognition module 1004. The
recognition module operates to derive information from, for
example, labels or printed markings on the object using e.g., OCR.
While it is to be understood that any suitable OCR technique or
tool may be used, in the following several exemplary OCR techniques
will be described.
[0119] Various embodiments provide the ability to isolate text
within a provided image and to turn it reliably into text, e.g.,
ASCII codes. A goal of OCR is to recognize machine printed text
using, e.g., a single font of a single size or even multi-font text
having a range of character sizes.
[0120] Some OCR techniques exploit the regularity of spatial
patterns. Techniques like template matching use the shape of
single-font characters to locate them in textual images. Other
techniques do not rely solely on the spatial patterns but instead
characterize the structure of characters based on the strokes used
to generate them. Despite the considerable variety in the
techniques employed, many suitable OCR systems share a similar set
of processing stages.
[0121] One OCR stage may include extraction of the character
regions from an image: This stage will typically use ancillary
information known in order to select image properties that are
sufficiently different for the text regions and the background
regions as the basis for distinguishing one from the other. One
common technique when the background is a known solid color (white
for instance) is to apply iterative dichotomies based on color
histograms. Other techniques might make use of known character
sizes or other spatial arrangements.
[0122] Another OCR stage may include segmentation of the image into
text and background. Once provided with image regions that contain
text the goal of this stage is to identify image pixels that belong
to text and those that belong to the background. The most common
technique used here is a threshold applied to the grayscale image.
The threshold value may be fixed using ancillary knowledge about
the application or by using measures calculated in the neighborhood
of each pixel to determine an adaptive local threshold.
[0123] Another OCR stage may include conditioning of the image: The
image segments resulting from segmentation may contain some pixels
identified as belonging to the wrong group. This stage consists of
a variety of techniques used to clean it up and delete noise.
[0124] Yet anther OCR stage may include segmentation of characters:
Some techniques will subsequently segment the input image into
regions that contain individual characters but other algorithms
will avoid this stage and proceed with character recognition
without prior character segmentation. This latter technique is
driven by the realization that in many cases character segmentation
turns out to be a more difficult problem than recognition
itself.
[0125] Some OCR stages include normalization of character size:
Once the image is segmented into characters, one may adjust the
size of the character regions so that the following stages can
assume a standard character size. Systems that rely on
size-independent topological features for their character
recognition stages might not require such normalization.
[0126] OCR systems typically include feature detection: Many
different feature detection techniques are known in the art. Some
template matching is used to find the whole character as a feature,
while other systems seek sub features of the characters. These may
include boundary outlines, the character skeleton or medial axis,
the Fourier or Wavelet coefficients of the spatial pattern, various
spatial moments and topological properties such as the number of
holes in a pattern.
[0127] A classification stage may be used to assign, to a character
region, the character whose properties best match the properties
stored in the feature vector of the region. Some systems use
structural classifiers consisting of a set of tests and heuristics
based on the designer's understanding of character formation. Other
classifiers take a statistical rather than structural approach,
relying on a set of training samples and using statistical
techniques to build a classifier. These approaches include the
Bayes decision rule, nearest neighbor lookups, decision trees, and
neural networks.
[0128] In a verification stage knowledge about the expected result
is used to check if the recognized text is consistent with the
expected text. Such verification may include confirming that the
extracted words are found in a dictionary, or otherwise match some
external source of information (e.g. if city information and zip
code information in a U.S. postal address match). This stage is
obviously application dependent.
[0129] In various embodiments, the recognition module 1004 may
employ any of the above described techniques, alone or in
combination.
[0130] Recognition of handwritten characters (sometimes referred to
as ICR) may, in some applications, be more challenging. In the
context of applications such as tablet computers or PDA, the ICR
engine will often take advantage of pen stroke dynamics. Of course
this type of information is not available from the optical capture
of a hand-written document. Such applications may require the
system to be restricted to a smaller number of permissible
characters (e.g. upper caps or numeral) and/or rely heavily on a
small lexicon
[0131] For example, when text is handwritten in cursive it is often
difficult to segment each letter separately so rather than
operating as an optical character recognition, an ICR system will
often operate as a "Word recognizer", looking to the best match
between the graphical object and a small lexicon of recognizable
words. In order to achieve a satisfactory recognition rate, this
lexicon might need to be as small as 10 words or so.
[0132] In various embodiments, the performance of an OCR system may
be increased by specializing to the task at hand by restricting its
lexicon or dictionary so that it can effectively recover from few
character recognition errors the same way a computer (e.g. running
a word processor) might be able to correct a typo.
[0133] Maintaining a restricted and dynamic lexicon is more
effective when a document has a rigid and known structure. Without
such structure it might not be possible to use a lexicon any
smaller than a dictionary for the language at hand.
[0134] Fortunately, as shown in FIG. 13 an address appearing on a
mail piece is typically a relatively highly structured a document.
This is why the USPS can OCR a large part of the machinable mail
pieces even when address are hand-written.
[0135] In typical embodiments, a proper usage of OCR should take
into account some typical shortcomings. Generality must be
considered versus accuracy. A single classifier might be trained to
get improved results in limited circumstances (a single font for
instance) but its performance will typically drop when the size of
its training set increases. Consequently, modern classifiers are in
fact conglomerates of classifiers coupled with a mechanism to
consolidate their results. This in turn will tend to further
increase the already substantial computational requirements of the
system if it is intended to cope with a large variety of fonts.
[0136] Non uniform backgrounds may present challenges. OCR
algorithms typically take advantage of the fact that the text is
presented on a uniform background that has sufficiently high
contrast between text and background colors. When the background is
not uniform, OCR recognition rates are substantially decreased. In
those cases and in order to remove a non-uniform background from
the image, additional preprocessing stages might be required prior
to the various ones we've presented above.
[0137] Image resolution should be considered. OCR technologies were
developed within the context of scanned physical documents.
Although optical scanning might lead to various artifacts such as
noise and slight skewing, these will also typically operate at
higher image resolutions (<200 dpi). As discussed above, imaging
module 1001 may provide images at such resolutions, e.g. by
employing digital cameras known in the art.
[0138] Most mail pieces will already convey some machine-readable
data (e.g. bar codes, postal marks) by the time it reaches an
enrollment device. In various embodiments, the enrollment device
may read these markings using OCR, or using additional sensors
(e.g. a barcode reader).
[0139] FIG. 4d shows the output display of an exemplary embodiment
of an enrollment device 100. The display shows the captured image
of a package placed on the device, along with information acquired
from labels and markings on the package using the OCR techniques
described above. This embodiment was able to accommodate OCR of
packages placed at an arbitrary angle on receiving surface 104,
using, for example, the rotation correction techniques described
above.
[0140] Information obtained using OCR is passed on for, for
example, address quality, meter enforcement, value added service
subsystems, and operator input. In some embodiments, the OCR
facility will be able to read documents such as passports, driver
licenses, credit cards, coupons, tickets, etc. Simply placing the
document anywhere on the receiving surface 104 will trigger a read
and document analysis. Form capture is also supported with the
ability to allow customers to, for example, present completed forms
for immediate OCR results available to the postal clerk. Certain
forms such as customs declarations can be handled much more
efficiently with this facility.
Dimension Capture Function
[0141] In typical applications, accurately determining the
dimensions of a package at enrollment may be crucial for
determining, for example, the rate of postage. For example, postal
rates may depend on an objects length, width, height, and/or
combinations thereof.
[0142] As noted above, during image acquisition and processing, one
or more dimensions of a package placed on an enrollment device may
be determined. For example, FIG. 4e shows an output display of an
exemplary embodiment of an enrollment device 100. The display shows
the captured image 401 of a package, a difference image 402, and a
Hough plane image 403 generated using the techniques described
above. As indicated in the captured image 401, the system has
successfully identified the edges of the face of the object imaged
by the device. This allows the device to calculate and output the
length and width of the package.
[0143] The height dimension is captured using, for example,
ultrasonic range finder 122, thereby providing complete dimensional
information. An ultrasonic transducer emits sound waves and
receives sound waves reflected by objects in its environment. The
received signals are processed to provide information about the
spatial location of the objects. For example, in the embodiment
shown in FIGS. 1a-1c, the rangefinder can determine the vertical
position of the top surface of the package 106 relative to the
receiving surface 104. One advantage of ultrasonic rangefinder over
optical rangefinders is that it is able to unambiguously detect
optically transparent surfaces (e.g. the glass surface 104 of FIGS.
1a-1c).
[0144] It is to be understood that, in various embodiments, other
suitable dimension capture techniques may be used. Some embodiments
may employ other types of rangefinders (e.g. optical sensors). In
some embodiments, the top (or other) surface of a package may be
located mechanically by bringing a sliding arm or a user held wand
in contact with the surface package, and detecting the position of
the arm or wand. In some embodiments, more than two dimensions of
the package may be determined based on captured image data, for
example, by stereoscopically imaging the object from multiple
perspectives.
[0145] Although the examples above generally include dimension
capture of rectangular objects, it is to be understood that the
techniques described above can be extended to objects of any
arbitrary shape.
RFID Function
[0146] If an item has an RFID tag it will be detected and read by
an RFID peripheral attached to or integrated with the enrollment
device 100. The acquired data is then available for further
processing and/or output to downstream applications.
Processing and User Interface Functions
[0147] As discussed above, the enrollment device may process the
myriad of captured data related to a package and output relevant
information to a user. In some embodiments, information is
displayed to a user through an interactive graphical user interface
(GUI). For example, as shown in FIG. 14, the user may navigate back
and forth through a series of screens 1401a, 1401b, and 1401c
using, for example, a mouse, keyboard, or touch screen device.
Referring to FIG. 14a, screen 1401a shows an image of the package
along with captured data. The user may confirm the captured
information and/or choose to proceed to screen 1401b, shown in
detail in FIG. 14b, for editing the captured data and/or adding
additional data. Once all relevant information about the package
has been captured and confirmed or otherwise entered, a further
screen 1401c presents various delivery service options.
[0148] In some embodiments an expert system employing "backward
chaining" logic may be employed to receive and analyze the wealth
of information coming from the enrolment device. As is known in the
art, in typical applications, backward chaining starts with a list
of goals (or a hypothesis) and works backwards from the consequent
to the antecedent to see if there is data available that will
support any of these consequents. An inference engine using
backward chaining would search the inference rules until it finds
one which has a consequent (Then clause) that matches a desired
goal. If the antecedent (If clause) of that rule is not known to be
true, then it is added to the list of goals (in order for your goal
to be confirmed you must also provide data that confirms this new
rule).
[0149] The system can use such techniques to generate multiple
service options based on the captured information and/or user
requirements. As shown in FIGS. 15a, 15b, and 15c, these options
may be organized and presented (e.g. to a customer or salesperson)
in a convenient fashion using, for example, a touch screen
interface.
[0150] FIG. 16 shows another example of a sequence of GUI
screens.
[0151] In some embodiments, USB and Ethernet connections will be
provided. Some embodiments will include additional USB, keyboard,
and display connections. In some embodiments the firmware/software
will support Simple Object Access Protocol/Service Oriented
Architecture Protocol (SOAP) calls. Some embodiments will support a
Web Server, rating engine, and/or maintenance facilities.
[0152] In some embodiments, an embedded computing platform, e.g.
processor 114, contained in or peripheral to the enrolment device
100 allows it to operate as a stand alone postage meter. In some
embodiments, the enrolment device 100 brings an intelligent item
assessment capability to the corporate mail room. Shippers can be
assured that the services they require will be correctly calculated
and that items shipped will be in full compliance with the terms of
service. Additionally, in some embodiments, the enrolment device
will be able to communicate directly with the post office allowing
billing directly from SAP, sales and marketing support, and
convenient automatic scheduling of pick ups. Rates and incentives
can be system wide, applied to a subset of customers, or even be
specific to an individual customer.
Display and Control Functions
[0153] In some embodiments, the main on-device control function is
presented by three OLED captioned buttons. The captions are dynamic
and are managed by the firmware. An application programming
interface (API) allows (possibly external) applications to control
the buttons when the firmware is not using them. Operational,
maintenance, and diagnostic functions are supported. If required,
the extension arm can have a display attached, for example, if
required by local regulation.
Additional Features
[0154] Various embodiments include one or more of the following
features: [0155] The embedded computing platform will have a PSD
(Postal Security Device) built in. [0156] The enrolment device will
use available information (e.g. from an intranet or internet
connection) to establish its location. [0157] The embedded
electronics will have a secure "black box" data recorder for audit
and control purposes. This capability can be remotely accessed
(e.g. via an intranet or internet connection). [0158] Cryptographic
capabilities consistent with export regulations will be available.
[0159] User management and tokens will be supported. Departmental
accounting is possible. SAP codes will be supported. [0160] Work
Flow Systems Integration and support for manufacturing systems will
be available. [0161] Dashboard facilities with remote access will
be supported. [0162] Automatic integration with dispatch centers
will be supported. [0163] Embedded wireless broadband will be
available. [0164] Ability to read full size checks and bill payment
forms. [0165] Ability to capture signed documents for onward
processing or item truncation. [0166] Extensive device support
including but not limited to; [0167] Card Readers, [0168] Printers,
[0169] Postal Label Printers, [0170] Interactive Customer Display,
[0171] Pin Input devices, [0172] Keyboards.
Exemplary Applications
[0173] Based on the ability to provide the general business
benefits described above, the devices techniques described have
commercial applications in the following market segments:
Managed Content
[0174] The evolution of the letter stream as a facility to carry
packets suitable for delivery to unattended delivery points
requires the addition of infrastructure allowing that activity. The
enrolment of such items requires the ability to capture all of the
information required for all items to be sent through the mail
stream as well as new value added services. The ability to tag
items with services such as cold stream, work flow notifications,
risk management, call center notifications, and conditional
deliveries will be required. In some embodiments, the enrollment
device would be located in locations such as pharmacies or
dedicated shipping centers where prescriptions were being prepared
for shipment to patients.
Postal Operators & Courier Companies:
[0175] For the highly automated postal operators and courier
companies, the enrollment device provides automated "front end"
data collection, leveraging their existing investment in systems
and technology.
[0176] For the low or non-automated strata of postal operators and
courier companies, the enrollment device provides a low-cost
automation solution for the capture and management of shipment
related information at their counter locations, eliminating a range
of paper-based processes and enabling integration with 3rd party
carriers and systems.
The Pharmaceutical Industry:
[0177] The enrollment device provides the pharmaceutical industry
with a means of automating the Provenance and Chain of Custody
aspects of their business.
Civil Defense:
[0178] The enrollment device provides a mechanism for the mass
distribution of products and services with a clear Chain of Custody
from point of Induction.
Goods Distribution Companies:
[0179] It is anticipated that Goods Distribution companies will
benefit from the ability to use the enrollment device to manage and
prepare their "one-to many" shipments.
[0180] One or more or any part thereof of the techniques described
above can be implemented in computer hardware or software, or a
combination of both. The methods can be implemented in computer
programs using standard programming techniques following the
examples described herein. Program code is applied to input data to
perform the functions described herein and generate output
information. The output information is applied to one or more
output devices such as a display monitor. Each program may be
implemented in a high level procedural or object oriented
programming language to communicate with a computer system.
However, the programs can be implemented in assembly or machine
language, if desired. In any case, the language can be a compiled
or interpreted language. Moreover, the program can run on dedicated
integrated circuits preprogrammed for that purpose.
[0181] Each such computer program is preferably stored on a storage
medium or device (e.g., ROM or magnetic diskette) readable by a
general or special purpose programmable computer, for configuring
and operating the computer when the storage media or device is read
by the computer to perform the procedures described herein. The
computer program can also reside in cache or main memory during
program execution. The analysis method can also be implemented as a
computer-readable storage medium, configured with a computer
program, where the storage medium so configured causes a computer
to operate in a specific and predefined manner to perform the
functions described herein.
[0182] A number of embodiments of the invention have been
described. Nevertheless, it will be understood that various
modifications may be made without departing from the spirit and
scope of the invention.
[0183] As used herein the terms "light" and "optical" and related
terms are to be understood to include electromagnetic radiation
both within and outside of the visible spectrum, including, for
example, ultraviolet and infrared radiation.
[0184] The examples above refer to a package received by the
enrollment device. It is to be understood that suitable item may be
received and enrolled, including: mail pieces, pharmaceutical
items, evidentiary items, documents, containers of any type,
etc.
[0185] A number of references have been incorporated in the current
application. In the event that the definition or meaning of any
technical term found in the references conflicts with that found
herein, it is to be understood that the meaning or definition from
the instant application holds.
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