U.S. patent application number 12/691725 was filed with the patent office on 2010-05-20 for natural language recognition using context information.
This patent application is currently assigned to Silverbrook Research Pty Ltd. Invention is credited to Paul Lapstun, Jonathon Leigh Napper, Kia Silverbrook.
Application Number | 20100125451 12/691725 |
Document ID | / |
Family ID | 34137114 |
Filed Date | 2010-05-20 |
United States Patent
Application |
20100125451 |
Kind Code |
A1 |
Napper; Jonathon Leigh ; et
al. |
May 20, 2010 |
Natural Language Recognition Using Context Information
Abstract
A method of recognising digital ink input by a user into a
computer-based digital ink recognition system is disclosed. The
user interacts with a paper-based document. The paper-based
document has disposed thereon coded data indicative of a particular
field of the paper-based document and of at least one reference
point of the paper-based document. An image sensor in a sensing
device captures images of at least some of the coded data when the
sensing device is placed in an operative position relative to the
paper-based document. The sensing device then decodes at least some
of the coded data to form indicating data indicative of the
identity of the field of the paper-based document containing the
coded data and at least one of a position and a movement of the
sensing device relative to the paper-based document. A server
receives the indicating data from the sensing device, and processes
the indicating data using a recognizer residing on the server to
produce intermediate format data. The intermediate format data is
then transmitted to an application which decodes the intermediate
format data into computer-readable format data using context
information associated with the paper-based document.
Inventors: |
Napper; Jonathon Leigh;
(Balmain, AU) ; Lapstun; Paul; (Balmain, AU)
; Silverbrook; Kia; (Balmain, AU) |
Correspondence
Address: |
SILVERBROOK RESEARCH PTY LTD
393 DARLING STREET
BALMAIN
2041
AU
|
Assignee: |
Silverbrook Research Pty
Ltd
|
Family ID: |
34137114 |
Appl. No.: |
12/691725 |
Filed: |
January 21, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10510392 |
Oct 7, 2004 |
7660466 |
|
|
PCT/AU2004/001088 |
Aug 16, 2004 |
|
|
|
12691725 |
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Current U.S.
Class: |
704/9 ; 382/159;
704/E11.001 |
Current CPC
Class: |
G06F 40/289 20200101;
G06F 40/279 20200101; G10L 15/30 20130101; G06K 9/6807 20130101;
G06K 9/00852 20130101; G06F 40/171 20200101 |
Class at
Publication: |
704/9 ; 382/159;
704/E11.001 |
International
Class: |
G06F 17/27 20060101
G06F017/27; G06K 9/62 20060101 G06K009/62 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 15, 2003 |
AU |
2003904350 |
Aug 15, 2003 |
AU |
2003904351 |
Claims
1. A method of recognising digital ink input by a user into a
computer-based digital ink recognition system, the user interacting
with a paper-based document, the paper-based document having
disposed thereon coded data indicative of a particular field of the
paper-based document and of at least one reference point of the
paper-based document, the method including the steps of: capturing
by an image sensor in a sensing device images of at least some of
the coded data when the sensing device is placed in an operative
position relative to the paper-based document; decoding by the
sensing device at least some of the coded data to form indicating
data indicative of the identity of the field of the paper-based
document containing the coded data and at least one of a position
and a movement of the sensing device relative to the paper-based
document; receiving in a server the indicating data from the
sensing device; processing the indicating data using a recognizer
residing on the server to produce intermediate format data;
receiving the intermediate format data in an application; decoding
the intermediate format data by the application into
computer-readable format data using context information associated
with the paper-based document.
2. The method as claimed in claim 1, wherein the particular field
of the paper-based document is associated with at least one zone of
the paper-based document, and the method includes identifying the
context information from the at least one zone.
3. The method as claimed in claim 1, wherein the intermediate
format data is a Directed Acyclic Graph (DAG) data structure.
4. The method as claimed in claim 3, wherein the DAG data structure
is a matrix containing the processing results of segments of the
indicating data.
5. The method as claimed in claim 1, wherein the intermediate
format data includes segmented time-series classifier data.
6. The method as claimed in claim 1, wherein the context
information is a user dictionary.
7. The method as claimed in claim 1, wherein the recognizer is
trained for a specific user.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation application of
U.S. patent application Ser. No. 10/510,392 filed on Oct. 7, 2004,
which is a 371 of PCT/AU04/001088 filed on Aug. 16, 2004, the
content of is herein incorporated by reference.
TECHNICAL FIELD
[0002] The present invention relates to a method of and system for
natural language recognition, and in particular, to a method of and
system for computer-based recognition of natural language data
implemented on a distributed computer network.
CROSS REFERENCES
[0003] Various methods, systems and apparatus relating to the
present invention are disclosed in the following granted US patents
and co-pending US applications filed by the applicant or assignee
of the present application: The disclosures of all of these granted
US patents and co-pending US applications are incorporated herein
by reference.
TABLE-US-00001 10/815,621 7,243,835 10/815,630 10/815,637
10/815,638 7,251,050 10/815,642 7,097,094 7,137,549 10/815,618
7,156,292 10/815,635 7,357,323 10/815,634 7,137,566 7,131,596
7,128,265 7,197,374 7,175,089 10/815,617 7,537,160 7,506,808
7,207,483 7,296,737 7,270,266 10/815,614 7,605,940 7,128,270
7,457,007 7,150,398 7,159,777 7,450,273 7,188,769 7,097,106
7,070,110 7,243,849 7,204,941 7,282,164 7,465,342 7,156,289
7,178,718 7,225,979 09/575,197 7,079,712 6,825,945 7,330,974
6,813,039 7,190,474 6,987,506 6,824,044 6,980,318 6,816,274
7,102,772 7,350,236 6,681,045 6,678,499 6,679,420 6,963,845
6,976,220 6,728,000 7,110,126 7,173,722 6,976,035 6,813,558
6,766,942 6,965,454 6,995,859 7,088,459 6,720,985 7,286,113
6,922,779 6,978,019 6,847,883 7,131,058 7,295,839 7,406,445
7,533,031 6,959,298 6,973,450 7,150,404 6,965,882 7,233,924
09/575,181 7,593,899 7,175,079 7,162,259 6,718,061 7,464,880
7,012,710 6,825,956 7,451,115 7,222,098 7,590,561 7,263,508
7,031,010 6,972,864 6,862,105 7,009,738 6,989,911 6,982,807
7,518,756 6,829,387 6,714,678 6,644,545 6,609,653 6,651,879
10/291,555 7,293,240 7,467,185 7,415,668 7,044,363 7,004,390
6,867,880 7,034,953 6,987,581 7,216,224 7,506,153 7,162,269
7,162,222 7,290,210 7,293,233 7,293,234 6,850,931 6,865,570
6,847,961 10/685,583 7,162,442 10/685,584 7,159,784 7,557,944
7,404,144 6,889,896 10/831,232 7,174,056 7,068,382 7,007,851
6,957,921 6,457,883 7,094,910 7,091,344 7,122,685 7,038,066
7,099,019 7,062,651 6,789,194 6,789,191 7,529,936 7,278,018
6,644,642 6,502,614 6,622,999 6,669,385 6,827,116 6,549,935
6,987,573 6,727,996 6,591,884 6,439,706 6,760,119 7,295,332
7,064,851 6,826,547 6,290,349 6,428,155 6,785,016 6,831,682
6,741,871 6,927,871 6,980,306 6,965,439 6,840,606 7,036,918
6,977,746 6,970,264 7,068,389 7,093,991 7,190,491 6,982,798
6,870,966 6,822,639 6,474,888 6,627,870 6,724,374 6,788,982
7,263,270 6,788,293 6,946,672 6,737,591 7,091,960 7,369,265
6,792,165 7,105,753 6,795,593 6,980,704 6,768,821 7,132,612
7,041,916 6,797,895 7,015,901 7,289,882 7,148,644 10/778,056
10/778,058 10/778,060 7,515,186 7,567,279 10/778,062 10/778,061
10/778,057 7,096,199 7,055,739 7,233,320 6,830,196 6,832,717
7,182,247 7,082,562 6,843,420 10/291,718 6,789,731 7,057,608
6,766,944 6,766,945 7,289,103 7,412,651 7,299,969 7,108,192
7,111,791 7,077,333 6,983,878 7,564,605 7,134,598 7,431,219
6,929,186 6,994,264 7,017,826 7,014,123 7,630,554 7,526,128
6,957,768 7,456,820 7,170,499 7,106,888 7,123,239 6,982,701
6,982,703 7,227,527 6,786,397 6,947,027 6,975,299 7,139,431
7,048,178 7,118,025 6,839,053 7,015,900 7,010,147 7,133,557
6,914,593 7,437,671 6,454,482 6,808,330 6,527,365 6,474,773
6,550,997 7,093,923 6,957,923 7,131,724
BACKGROUND ART
[0004] Recent advances in pattern classification have enabled the
development of sophisticated software systems that can recognize
natural language data (i.e. natural language user input) such as
speech (see for example L. Rabiner and B. Juang, "Fundamentals of
Speech Recognition", Prentice Hall, Englewood Cliffs, N.J., 1993)
or handwriting (see for example G. Lorette, "Handwriting
Recognition or Reading? Situation At The Dawn of the 3rd
Millennium", Advances In Handwriting Recognition, Series in Machine
Perception and Artificial Intelligence, Vol. 34, pp. 3-15, World
Scientific Publishing Co. 1999).
[0005] These applications allow users to communicate with a
computerised system in a natural and convenient way, and permit the
automation of tasks that previously required human input. Some
examples of such applications include interactive voice response
(IVR) systems, automated cheque-processing systems and automated
form data-entry systems.
[0006] In addition, the growth of networked computing and the
Internet has enabled the development of complex distributed
systems, and the existence of open, standardized protocols has
allowed the integration of end-user devices, centralized servers,
and applications. An example of a three-tiered distributed system
architecture is depicted in FIG. 1 (prior art), illustrating a
system 100 which includes a client layer 110, network layer 120 and
application layer 130. Client device 140 communicates with one or
more servers 150 which in turn communicate with one or more
applications 160. The combination of distributed computing and
pattern recognition techniques has made possible the development of
systems such as Netpage.TM. by Silverbrook Research Pty Ltd, an
interactive paper-based interface to online information. Systems
such as this give users the ability to interact with information
from any location that provides network connectivity (including
wireless network access) using familiar human-communication
techniques such as handwriting or speech.
[0007] The basic processing steps of presently known pattern
recognition systems are depicted in FIG. 2 (prior art). Processing
begins when an input device 210 generates a signal 220 that is to
be recognized by the system 100 (that is, to be classified as
belonging to a specific class or sequence of class elements).
Usually, one or more pre-processing procedures 230 are applied to
remove noise and produce a normalized signal 240, which is then
segmented 250 to produce a stream of primitive elements 260
required for a classification procedure 270. Note that often this
segmentation 250 is "soft", meaning that a number of potential
segmentation points are located, and the final segmentation points
are resolved during classification 270 or context processing
290.
[0008] The segmented signal 260 is then passed to a classifier 270
where a representative set of features is extracted from the signal
and used in combination with a pre-defined model 275 of the input
signal to produce a set of symbol hypotheses 280. These hypotheses
280 give an indication of the probability that a sequence of
segments within the signal represent a basic symbolic element (e.g.
letter, word, phoneme, etc.). After classification 270, the
context-processing module 290 uses the symbol hypotheses 280
generated by the classifier 270 to decode the signal according to a
specified context model 295 (such as a dictionary or character
grammar). The result 297 produced by the context processing 290 is
passed to the application 299 for interpretation and further
processing.
[0009] Natural language input is inconsistent, noisy, and
ambiguous, leading to potential recognition and decoding errors.
However, high recognition accuracy is required for pattern
recognition applications to operate successfully, since mistakes
can be expensive and frustrating to users. As a result, recognition
systems should make use of as much contextual information as
possible to increase the possibility of correctly recognizing the
natural language input. For example, when recognizing a signal that
must represent a country name, the recognition system can use a
pre-defined list of valid country names to guide the recognition
procedure. Similarly, when recognizing a phone number, a limited
symbol set (i.e. digits) can be used to constrain the recognition
results. The problem domain for many pattern recognition systems is
inherently ambiguous (i.e. many of the input patterns encountered
during processing cannot be accurately classified without further
information from a different source).
[0010] The following discussion refers to handwriting by way of
background information, however, the present invention should not
be considered to be limited to application to only handwriting as
the form of natural language data input.
[0011] Digital ink is a digital representation of the information
generated by a pen-based input device. Generally, digital ink is
structured as a sequence of strokes that begin when the pen device
makes contact with a drawing surface and ends when the pen-based
input device is lifted. Each stroke comprises a set of sampled
coordinates that define the movement of the pen-based input device
whilst the pen-based input device is in contact with the drawing
surface.
[0012] As an example, one of the major issues faced in the
development of highly accurate handwriting recognition systems is
the inherent ambiguity of handwriting (e.g. the letters `u` and
`v`, `t` and `f`, and `g` and `y` are often written with a very
similar appearance and are thus easily confused). Human readers
rely on contextual knowledge to correctly decode handwritten text,
and as a result a large amount of research has been directed at
applying syntactic and linguistic constraints to handwritten text
recognition (see for example: H. Beigi and T. Fujisaki, "A
Character Level Predictive Language Model and Its Application to
Handwriting Recognition", Proceedings of the Canadian Conference on
Electrical and Computer Engineering, Toronto, Canada, Sep. 13-16,
1992; U. Marti and H. Bunke, "Handwritten Sentence Recognition",
Proceedings of the 15th International Conference on Pattern
Recognition, Barcelona, Spain, Volume 3, pp. 467-470, 2000; D.
Bouchaffra, V. Govindaraju, and S. Srihari, "Postprocessing of
Recognized Strings Using Nonstationary Markovian Models", IEEE
Transactions Pattern Analysis and Machine Intelligence, 21(10), pp.
990-999, October 1999; J. Pitrelli and E. Ratzlaff, "Quantifying
the Contribution of Language Modeling to Writer-Independent On-line
Handwriting Recognition", Proceedings of the Seventh International
Workshop on Frontiers in Handwriting Recognition, Amsterdam,
September 11-13, 2000; R. Srihari, "Use of Lexical and Syntactic
Techniques in Recognizing Handwritten Text", ARPA Workshop on Human
Language Technology, Princeton, N.J., March 1994; and L. Yaeger, B.
Webb, and R. Lyon, "Combining Neural Networks and Context-Driven
Search for On-Line, Printed Handwriting Recognition in the Newton",
AI Magazine, Volume 19, No. 1, pp. 73-89, AAAI 1998).
[0013] The increasing use of pen-based computing and the emergence
of paper-based interfaces to networked computing resources (see for
example: Anoto, "Anoto, Ericsson, and Time Manager Take Pen and
Paper into the Digital Age with the Anoto Technology", Press
Release, 6 Apr. 2000; and Y. Chans, Z. Lei, D. Lopresti, and S.
Kung, "A Feature Based Approach For Image Retrieval by Sketch",
Proceedings of SPIE Volume 3229: Multimedia Storage and Archiving
Systems II, 1997) has highlighted the need for techniques to
interpret digital ink. Pen-based computing allows users to interact
with applications.
[0014] As a result of the progress in pen-based interface research,
handwritten digital ink documents, represented by time-ordered
sequences of sampled pen strokes, are becoming increasingly popular
(J. Subrahmonia and T. Zimmerman: Pen Computing: Challenges and
Applications. Proceedings of the ICPR, 2000, pp. 2060-2066).
Handwriting typically involves writing in a mixture of writing
styles (e.g. cursive, discrete, run-on etc.), a variety of fonts
and scripts and different layouts (e.g. mixing drawings with text,
various text line orientations etc.).
[0015] Presently, handwriting recognition accuracy remains
relatively low, and the number of errors introduced by recognition
(both for the database entries and for the handwritten query) means
that present techniques do not work well. The process of converting
handwriting into text results in the loss of a significant amount
of information regarding the general shape and dynamic properties
of the ink. In many handwriting styles (particularly cursive
writing), the identification of individual characters is highly
ambiguous.
[0016] Similar work has been performed in the field of speech
recognition, natural language processing, and machine
translation.
[0017] Some known natural language recognition systems currently
exist. Paragraph, Inc. offers a network-based distributed
handwriting recognition system called "NetCalif" (ParaGraph,
Handwriting Recognition for Internet Connected Device, November
1999) that is based on their Calligraphy handwriting recognition
software. The user's natural handwriting--cursive, print, or a
combination of both--is captured by client software, then
transmitted from an Internet-connected device to the NetCalif
servers where it is converted and returned as typewritten text to
the client device.
[0018] Philips has developed "SpeechMagic", a client/server-based,
professional speech recognition software package (Philips,
SpeechMagic 4.0, 2000). This system supports specialized
vocabularies (called ConTexts) and dictation, recognition, and
correction can be done, independently of the location, across a
LAN, WAN, or the Internet.
[0019] In a networked information or data communications system, a
user has access to one or more terminals which are capable of
requesting and/or receiving information or data from local or
remote information sources. The information source, in the present
context, may be a database associated with an application. In such
a communications system, a terminal may be a type of processing
system, computer or computerised device, personal computer (PC),
mobile, cellular or satellite telephone, mobile data terminal,
portable computer, Personal Digital Assistant (PDA), pager, thin
client, or any other similar type of digital electronic device. The
capability of such a terminal to request and/or receive information
or data can be provided by software, hardware and/or firmware. A
terminal may include or be associated with other devices, for
example a pen-based input device for handwriting input or a
microphone for speech input.
[0020] An information source can include a server, or any type of
terminal, that may be associated with one or more storage devices
that are able to store information or data, such as digital ink,
for example in one or more databases residing on a storage device.
The exchange of information (i.e., the request and/or receipt of
information or data) between a terminal and an information source,
or other terminal(s), is facilitated by a communication means. The
communication means can be realised by physical cables, for example
a metallic cable such as a telephone line, semi-conducting cables,
electromagnetic signals, for example radio-frequency signals or
infra-red signals, optical fibre cables, satellite links or any
other such medium or combination thereof connected to a network
infrastructure.
[0021] The reference to any prior art in this specification is not,
and should not be taken as, an acknowledgment or any form of
suggestion that such prior art forms part of the common general
knowledge.
DISCLOSURE OF INVENTION
[0022] According to an aspect of the present invention there is
provided a method of recognising digital ink input by a user into a
computer-based digital ink recognition system, the user interacting
with a paper-based document, the paper-based document having
disposed therein or thereon coded data indicative of a particular
field of the paper-based document and of at least one reference
point of the paper-based document, the method including the steps
of:
[0023] capturing by an image sensor in a sensing device images of
at least some of the coded data when the sensing device is placed
in an operative position relative to the paper-based document;
[0024] decoding by the sensing device at least some of the coded
data to form indicating data indicative of the identity of the
field of the paper-based document containing the coded data and at
least one of a position and a movement of the sensing device
relative to the paper-based document;
[0025] receiving in a server the indicating data from the sensing
device;
[0026] processing the indicating data using a recognizer residing
on the server to produce intermediate format data;
[0027] receiving the intermediate format data in an application;
decoding the intermediate format data by the application into
computer-readable format data using context information associated
with the paper-based document. Other aspects are also
disclosed.
BRIEF DESCRIPTION OF FIGURES
[0028] The present invention should become apparent from the
following description, which is given by way of example only, of a
preferred but non-limiting embodiment thereof, described in
connection with the accompanying figures.
[0029] FIG. 1 (prior art) illustrates a distributed system
architecture;
[0030] FIG. 2 (prior art) illustrates a flow chart of basic pattern
recognition steps;
[0031] FIG. 3 illustrates an example processing system able to be
used as a server to house a recognizer, according to a particular
embodiment of the present invention;
[0032] FIG. 4 illustrates an example distributed recognition
system, according to a particular embodiment of the present
invention;
[0033] FIG. 5 illustrates an example of ambiguous handwriting input
for "clog"/"dog";
[0034] FIG. 6 illustrates an example of ambiguous handwriting input
for "tile"/"lite";
[0035] FIG. 7 illustrates an example recognition scenario,
according to a particular embodiment of the present invention;
[0036] FIG. 8 illustrates an example recognizer selection scenario,
according to a particular embodiment of the present invention;
[0037] FIG. 9 illustrates an example recognizer training scenario,
according to a particular embodiment of the present invention;
[0038] FIG. 10 illustrates an example recognizer registration
scenario, according to a particular embodiment of the present
invention.
MODES FOR CARRYING OUT THE INVENTION
[0039] The following modes, given by way of example only, are
described in order to provide a more precise understanding of the
subject matter of the present invention.
[0040] A particular embodiment of the present invention can be
realised using a processing system, an example of which is shown in
FIG. 3. In particular, the processing system 300 generally includes
at least one processor 302, or processing unit or plurality of
processors, memory 304 and at least one output device 308, coupled
together via a bus or group of buses 310. At least one storage
device 314 which houses at least one database 316 can also be
provided, which may be remote and accessed via a network. The
memory 304 can be any form of memory device, for example, volatile
or non-volatile memory, solid state storage devices, magnetic
devices, etc. The processor 302 could include more than one
distinct processing device, for example to handle different
functions within the processing system 300.
[0041] Input device 306, for example a pen-based input device or a
microphone, is normally remote to the system 300. Input device 306
is used by a user to generate natural language data 318 which is
preferably transmitted over network 307 to system 300 for
processing. Output device 308 produces or generates intermediate
format data 320, for example for transmission over a network, to be
transmitted to application 324, which could be remote or local to
the system 300. The storage device 314 can be any form of data or
information storage means, for example, volatile or non-volatile
memory, solid state storage devices, magnetic devices, etc.
[0042] In use, the processing system 300 may be a server and is
adapted to allow data or information to be stored in and/or
retrieved from, via wired or wireless communication means, the at
least one database 316, which may be remote and accessed via a
further network. The processor 302 receives natural language data
318 from input device 306, preferably via network 307, and outputs
intermediate format data 320 by utilising output device 308, for
example a network interface. The application 324 may return decoded
data to the processing system. The application 324 may cause
information to be printed, for example on a Netpage.TM. printer, at
a user's location. More than one input device 306 can be provided.
It should be appreciated that the processing system 300 may be any
form of terminal, server, specialised hardware, or the like. The
processing system 300 may be a part of a networked communications
system. Also, the application 324 may initiate transfer of natural
language data 318 from the input device 306 to server 300.
[0043] In a particular embodiment, the server 300 is part of a
system for computer-based recognition of natural language data, the
system implemented on a network and comprising: the input device
306 to obtain natural language data; server 300 to receive the
natural language data 318 via a network 307; a recognizer residing
on the server 300 to process, in processor 302, the natural
language data 318 to produce intermediate format data 320; and, an
application 324 to receive the intermediate format data 320 and to
decode the intermediate format data 320 into computer-readable
format data using context information associated with the
application 324.
[0044] The following example provides a more detailed discussion of
a particular embodiment of the present invention. The example is
intended to be merely illustrative and not limiting to the scope of
the present invention.
[0045] In a particular preferred embodiment, the present invention
is configured to work with the Netpage.TM. networked computer
system, a detailed description of which is given in the applicant's
co-pending applications, including in particular, PCT Publication
No. WO0242989 entitled "Sensing Device" filed 30 May 2002, PCT
Publication No. WO0242894 entitled "Interactive Printer" filed 30
May 2002, PCT Publication No. WO0214075 "Interface Surface Printer
Using Invisible Ink" filed 21 Feb. 2002, PCT Publication No.
WO0242950 "Apparatus For Interaction With A Network Computer
System" filed 30 May 2002, and PCT Publication No. WO03034276
entitled "Digital Ink Database Searching Using Handwriting Feature
Synthesis" filed 24 Apr. 2003.
[0046] It will be appreciated that not every implementation will
necessarily embody all or even most of the specific details and
extensions described in these applications in relation to the basic
system. However, the system is described in its most complete form
to assist in understanding the context in which the preferred
embodiments and aspects of the present invention operate.
[0047] In brief summary, the preferred form of the Netpage system
provides an interactive paper-based interface to online information
by utilizing pages of invisibly coded paper and an optically
imaging pen. Each page generated by the Netpage system is uniquely
identified and stored on a network server, and all user interaction
with the paper using the Netpage pen is captured, interpreted, and
stored. Digital printing technology facilitates the on-demand
printing of Netpage documents, allowing interactive applications to
be developed. The Netpage printer, pen, and network infrastructure
provide a paper-based alternative to traditional screen-based
applications and online publishing services, and supports
user-interface functionality such as hypertext navigation and form
input.
[0048] Typically, a printer receives a document from a publisher or
application provider via a broadband connection, which is printed
with an invisible pattern of infrared tags that each encodes the
location of the tag on the page and a unique page identifier. As a
user writes on the page, the imaging pen decodes these tags and
converts the motion of the pen into digital ink. The digital ink is
transmitted over a wireless channel to a relay base station, and
then sent to the network for processing and storage. The system
uses a stored description of the page to interpret the digital ink,
and performs the requested actions by interacting with an
application.
[0049] Applications provide content to the user by publishing
documents, and process the digital ink interactions submitted by
the user. Typically, an application generates one or more
interactive pages in response to user input, which are transmitted
to the network to be stored, rendered, and finally printed as
output to the user. The Netpage system allows sophisticated
applications to be developed by providing services for document
publishing, rendering, and delivery, authenticated transactions and
secure payments, handwriting recognition and digital ink searching,
and user validation using biometric techniques such as signature
verification.
[0050] Distributed Pattern Recognition
[0051] An example architecture for a distributed pattern
recognition system 400 is depicted in FIG. 4. In the example, a
signal 410 is recorded by an input device 415 at a client layer 420
and transmitted over a network to a server (network layer 430) for
recognition by a recognizer 440, with the intermediate results 445
transmitted back to the client layer 420 or a third party
application 450 on an application layer 455 for interpretation and
processing. One advantage of this approach is that client devices
415 and distributed applications 450 do not require the significant
computing resources commonly needed to perform natural language
pattern recognition, and the network servers that perform the
recognition are not subject to the resource constraints that are
inherent in many client devices 415 (e.g. mobile phones,
personal-digital assistants, imaging pens, etc.). As a result,
network servers are able to use extremely processor- and/or
memory-intensive techniques to improve recognition accuracy, and
can use hardware optimised to perform the specific recognition
task.
[0052] Performing pattern recognition on a centralized server (e.g.
processing system 300) also offers an advantage to
pattern-recognition systems that employ user-specific adaptation to
achieve higher recognition rates. For example, some handwriting
recognition techniques develop a handwriting model for each user of
the system based on previous recognition results, which is then
used to improve the future accuracy of the system for that user
(see for example L. Schomaker, H. Teulings, E. Helsper, and G.
Abbink, "Adaptive Recognition Of Online, Cursive Handwriting",
Proceedings of the Sixth International Conference on Handwriting
and Drawing. Paris, July, 4-7 Telecom, (pp. 19-21), 1993 and S.
Connell and A. K. Jain, "Writer Adaptation of Online Handwritten
Models," Proc. 5th International Conference on Document Analysis
and Recognition, Bangalore, India, pp. 434-437, September
1999).
[0053] This adaptation is more effective if a single server, or set
of servers, performs all recognition for a user (rather than a
large number of individual applications each performing their own
recognition), since the server is able to perform adaptation based
on the input generated by all applications. In addition to this,
centralized server-based pattern recognition simplifies the
management of the recognition system 400 by allowing recognizers to
be reconfigured and upgraded without interaction with the
distributed client devices 415 and applications 450, and allows
training and test data to be easily collected.
[0054] However, the information required to perform the context
processing stage of a pattern recognition system is generally
application specific and is often very large (e.g. entries in a
large application-specific database), making it impractical to
transmit the context information to a centralized server for
processing. A solution to this problem is to use a mechanism for
distributed recognition as depicted in FIG. 4. When a user
generates a signal (i.e. natural language data) 410 to be
recognized and processed by an application, the signal 410 is
submitted to a distributed server for processing. The server
performs processing steps such as pre-processing, segmentation, and
classification (see FIG. 2), but does not use a context model to
decode the result (or only performs partial decoding as described
in the following discussion). Rather, the intermediate recognition
results (i.e. intermediate format data) are returned or sent to the
application allowing the application to apply any arbitrarily
complex and domain-specific context processing to decode the
signal.
[0055] Symbol DAG
[0056] One method of returning the intermediate recognition results
(i.e. intermediate format data) to an application is to use a
symbol DAG (Directed Acyclic Graph), which is a generic data
structure that contains symbol and associated scores as vertices,
and valid transitions between symbols as edges. The structure can
be implemented as a two-dimensional array of elements, each of
which defines the output generated by the pattern classifier for a
single segment of the signal and the associated valid transitions
for that segment. This structure represents all the potential
recognition alternatives that may be derived from the input signal
based on the results of the classifier. The application uses this
structure, in combination with a context model, to decode the input
signal.
[0057] The symbol DAG is equivalent to a matrix where each column
contains the results of the classification of a single segment of
the input signal. Each element in the column represents the
probability that the classified segment is a particular symbol, and
includes an offset that indicates the next possible segment
(column) in the input signal that can follow this symbol. Thus, the
matrix represents all the possible decoding paths based on the
output of the pattern classifier. These paths and associated
classification scores can be combined with a context model to fully
decode the input signal.
[0058] Note that the symbol DAG is applicable in any pattern
recognition task where a sequence of classification results is
decoded using a context or set of constraints. The symbols
contained in the symbol DAG may be any primitive element that is
generated as the output of a pattern classifier, including the
output from a time-series classifier. Examples of such recognition
systems include handwriting and speech recognition, protein
sequencing (see A. C. Camproux, P. Tuffery, S. Hazout, "Hidden
Markov Model Approach For Identifying The Modular Framework Of The
Protein Backbone", Protein engineering, 12(12), pp. 1063, December
1999), image processing and computer vision (see Y. He, A. Kundu,
"2-D Shape Classification Using Hidden Markov Model", IEEE
Transactions on Pattern Analysis, 13(11), November 1991), and
econometrics (see T. Ryden, T. Terasvirta, S. Asbrink, "Stylized
Facts of Daily Return Series and the Hidden Markov Model", Journal
of Applied Econometrics, 13(3), pp. 217, May 1998).
[0059] Symbol DAG Example
[0060] As an example, Table 1 shows a symbol DAG that represents
the output from a handwritten character recognizer generated by the
ambiguous text given in FIG. 5. In this example, the recognizer has
found two possible character segmentation arrangements, as depicted
by the two rows in the symbol DAG. Note that in the examples, the
symbol scores are given as probabilities; however, an actual
implementation may typically use log-probabilities (i.e. the
base-10 logarithm of the probability result) to improve the
performance of context processing and to avoid overflow and
underflow problems that occur when multiplying probabilities using
finite precision floating-point operations.
[0061] To decode the alternatives, the context processor starts
with the first entry in the DAG (i.e. the character `c`). The score
for this entry is added to the accumulated total (since
log-probabilities are added rather than multiplied), and processing
moves to the column given by the offset value in the entry (in this
example, column 1). In column 1, two alternatives exist (i.e. "cl"
or "cb"), and the scores for these alternatives are found by adding
the scores to the previous total. The decoding continues until the
end of the DAG is reached. Similarly, the second entry in column 0
(i.e. the character `d`) is decoded; note however, that column 1 is
skipped in this traversal of the DAG, as indicated by the offset
value of 2 in the character score entry. This is due to the letter
`d` being constructed using two strokes, and thus the recognition
of the letters `l` and `b` cannot be valid in this alternative.
Thus, the potential decoding alternatives in this example are:
c log=0.7*0.8*1.0*1.0=0.56
cbg=0.7*0.2*1.0=0.14
dog=0.3*1.0*1.0=0.30
[0062] These values can now be combined with a language model or
other contextual information to select the most likely word.
TABLE-US-00002 TABLE 1 Example DAG for "clog"/"dog" ambiguity 0 1 2
3 Character c l o g Offset 1 2 3 0 Score 0.7 0.8 1.0 1.0 Character
d b Offset 2 3 Score 0.3 0.2
[0063] The DAG structure must ensure that strokes are assigned to
an individual letter only once. To do this, alternate paths must be
defined to ensure that if a stroke is assigned to a letter, no
subsequent letter may use that stroke in its construction. An
example of this is given in FIG. 6, with the derived DAG depicted
in Table 2. In this example, the short, horizontal marks can
potentially be recognized as crossbar elements of a letter `t`, or
diacritical marks for the letter `i`. However, if a marking is used
as a crossbar, it cannot subsequently be used as a diacritical. The
potential decoding alternatives in this example are:
tile=0.6*1.0*0.6*1.0=0.36
tite=0.6*1.0*1.0*1.0=0.60
lite=0.4*1.0*1.0*1.0=0.40
[0064] These values can now be combined with a language model to
select the most likely word.
TABLE-US-00003 TABLE 2 Example DAG for "lite"/"tile" ambiguity 0 1
2 3 4 5 Character t i i t l e Offset 1 4 3 5 5 -- Score 0.6 1.0 1.0
1.0 0.6 1.0 Character l t Offset 2 5 Score 0.4 0.4
[0065] Additionally, the character value of a DAG entry can be set
to zero, indicating a NUL character (i.e. a character that does not
change the text, but will modify the text probability). This allows
word break positions (i.e. spaces) to be modeled as a SPACE/NUL
pair, indicating that there is a certain probability that a space
appears at that point in the DAG. For example:
TABLE-US-00004 TABLE 3 Example DAG for SPACE/NUL pair 0 1 2
Character a NUL b Offset 1 1 -- Score 1.0 0.6 1.0 Character SPACE
Offset 1 Score 0.4
[0066] The potential decoding alternatives in this example are:
ab=1.0*0.6*1.0=0.6
a b=1.0*0.4*1.0=0.4
[0067] Distributed Recognizer Management
[0068] Referring to FIGS. 7 and 8, a distributed recognition system
700 may support a number of different recognizers 440 that are
controlled by a distributed recognition management system or
recognition manager 710. These recognizers 440 can include systems
capable of supporting different classes of recognition, such as
different languages, dialects, or accents, or cursive or boxed
input for handwriting systems. When an application 450 requires a
recognition task to be performed, the application 450 first queries
720 the recognition manager 710 to find a recognizer 440 that
matches the parameters of the input to be recognized (as depicted
in FIG. 8). The recognition manager 710 then queries 730 each
recognizer 440 to find a recognizer that supports the parameters
specified by the application 450. When a recognizer 440 indicates
support 740 (as opposed to no support 750 from recognizer 440a in
FIG. 8) for the specified parameter set, the enumeration ends and
the selected recognizer 440 (in the case of FIG. 8 recognizer 440b)
is passed 760 to the application 450. Note that the individual
recognizers 440 do not need to be centralized and may be
distributed throughout the system 700, since the recognition
manager 710 acts as a controller for the set of recognizers 440.
The application 450 can then request processing by the selected
recognizer by passing or directing 770 the signal and parameters to
the selected recognizer 440. Intermediate format data 445, i.e. a
symbol lattice, is returned to the application 450 and the
application 450 can return a response 780 to the input device
415.
[0069] User-Specific Dictionaries
[0070] Distributed recognition systems can also support user
dictionaries, which are user-specific word lists (and possibly
associated a-priori probabilities) that include words that a user
writes frequently but which are unlikely to appear in a standard
dictionary (examples include company names, work or personal
interest specific terms, etc.). User dictionaries can be stored and
managed centrally so that words added to the dictionary when using
one application are available to all applications for context
processing. Obviously, applications can manage and use their own
local user-specific dictionaries if required, since they have full
control over context decoding.
[0071] When an application requires the recognition of a signal
that may contain words found in the user dictionary (e.g. standard
handwritten text input such as the subject line of an e-mail or an
arbitrary voice message), the centralized recognition system
generates the usual intermediate recognition results to be returned
to the application for context decoding. However, in addition to
this it decodes the intermediate results using the user-dictionary
as a language model, the result of which is also returned to the
application. These two intermediate results structures can be
combined by the application during its context decoding to generate
a final decoding that includes the user-specific dictionary
information.
[0072] User-Specific Training
[0073] Distributed recognition systems may also support
user-specific training for a recognizer 440, as depicted in FIG. 9.
The data generated by a user-specific recognition training
application is submitted 910 to the centralized recognition manager
710, which stores 920 the data in a database 930. The recognition
manager 710 then enumerates all recognizers 440 to determine if
they support the data format as defined by the parameters
associated with the training data, and if so (True signal 940),
submits the training data 950 to the recognizer 440 for
user-specific training
[0074] When an existing recognizer is upgraded or a new recognizer
is added to the system, the recognition manager 710 queries 1010
the training database 930 to determine if any training data 1020 of
the format required by the recognizer 440 exists. If so, the
training data 1020 is submitted to the newly registered recognizer
440 for processing, as depicted in FIG. 10.
[0075] The invention may also be said to broadly consist in the
parts, elements and features referred to or indicated herein,
individually or collectively, in any or all combinations of two or
more of the parts, elements or features, and wherein specific
integers are mentioned herein which have known equivalents in the
art to which the invention relates, such known equivalents are
deemed to be incorporated herein as if individually set forth.
[0076] Although a preferred embodiment has been described in
detail, it should be understood that various changes,
substitutions, and alterations can be made by one of ordinary skill
in the art without departing from the scope of the present
invention. We claim:
* * * * *