U.S. patent application number 12/106779 was filed with the patent office on 2008-09-18 for rollup functions for efficient storage, presentation, and analysis of data.
This patent application is currently assigned to RAF TECHNOLOGY, INC.. Invention is credited to Stephen E.M. Billester, David Justin Ross, Brent R. Smith.
Application Number | 20080228469 12/106779 |
Document ID | / |
Family ID | 26823413 |
Filed Date | 2008-09-18 |
United States Patent
Application |
20080228469 |
Kind Code |
A1 |
Ross; David Justin ; et
al. |
September 18, 2008 |
ROLLUP FUNCTIONS FOR EFFICIENT STORAGE, PRESENTATION, AND ANALYSIS
OF DATA
Abstract
Methods of organizing a series of sibling data entities in a
digital computer are provided for preserving sibling ranking
information associated with the sibling data entities and for
attaching the sibling ranking information to a joint parent of the
sibling data entities to facilitate on-demand generation of ranked
parent candidates. A rollup function of the present invention
builds a rollup matrix (126) that embodies information about the
sibling entities and the sibling ranking information and provides a
method for reading out the ranked parent candidates from the rollup
matrix in order of their parent confidences (141). Parent
confidences are based on the sibling ranking information, either
alone or in combination with n-gram dictionary ranking or other
ranking information.
Inventors: |
Ross; David Justin;
(Redmond, WA) ; Billester; Stephen E.M.; (Redmond,
WA) ; Smith; Brent R.; (Redmond, WA) |
Correspondence
Address: |
Stolowitz Ford Cowger LLP
621 SW Morrison St, Suite 600
Portland
OR
97205
US
|
Assignee: |
RAF TECHNOLOGY, INC.
Redmond
WA
|
Family ID: |
26823413 |
Appl. No.: |
12/106779 |
Filed: |
April 21, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10410015 |
Apr 8, 2003 |
7379603 |
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12106779 |
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09528749 |
Mar 20, 2000 |
6597809 |
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10410015 |
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60125352 |
Mar 19, 1999 |
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60125257 |
Mar 19, 1999 |
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Current U.S.
Class: |
704/10 ; 382/177;
704/E15.018; 707/E17.011 |
Current CPC
Class: |
G06K 9/6255 20130101;
G06F 16/283 20190101; G06F 16/9024 20190101; G06K 2209/01 20130101;
G06K 9/723 20130101 |
Class at
Publication: |
704/10 ; 382/177;
704/E15.018 |
International
Class: |
G06F 17/21 20060101
G06F017/21; G06K 9/72 20060101 G06K009/72 |
Claims
1. A computer-implemented system for organizing a set of sibling
entities each having one or more child possibilities, at least one
of the sibling entities including multiple child possibilities
having a relative rank or confidence value and_from which multiple
parent candidates can be generated, each of the parent candidates
having a relative rank, and for generating an ordered series of
parent candidates from the child possibilities, comprising: a means
for initializing a plurality of nodes in a computer-readable data
storage medium for storing the child possibilities of the sibling
entities; a means for loading the sibling entities into the nodes
to form a rollup matrix having an organization that represents the
relative ranking of the parent candidates; and a means for reading
from the nodes to generate a series of parent candidates in order
of their ranking.
2. The system of claim 1, further comprising: a means for
calculating a parent candidate confidence for at least some of the
parent candidates; a means for storing the parent candidate
confidences in the rollup matrix in association with the
corresponding parent candidates; and in which the means for reading
from the nodes generates the series of parent candidates based on
the stored parent candidate confidences.
3. The system of claim 1, further comprising a means for comparing
the generated parent candidates against a dictionary.
4. The system of claim 1 in which: at least one of the sibling
entities includes a nested child matrix having an entry point; and
the means for loading includes a means for loading the nested child
matrix into one or more of the nodes, a means for creating a
pointer to the entry point, and a means for storing the pointer in
the rollup matrix.
5. A computer-implemented method for organizing a set of sibling
entities each having one or more child possibilities, at least one
of the sibling entities including multiple child possibilities
having a relative rank or confidence value and from which multiple
parent candidates can be generated, each of the parent candidates
having a relative rank, and for generating an ordered series of
parent candidates from the child possibilities, comprising:
initializing a plurality of nodes in a computer-readable data
storage medium for storing the child possibilities of the sibling
entities; loading the sibling entities into the nodes to form a
rollup matrix having an organization that represents the relative
ranking of the parent candidates; and reading from the nodes to
generate a series of parent candidates in order of their ranking;
and outputting at least one of the parent candidates.
6. The method of claim 5, further comprising: calculating a parent
candidate confidence for at least some of the parent candidates;
storing the parent candidate confidences in the rollup matrix in
association with the corresponding parent candidates; and reading
from the nodes generates the series of parent candidates based on
the stored parent candidate confidences.
7. The method of claim 5, further comprising comparing the
generated parent candidates against a dictionary.
8. The method of claim 5 in which: at least one of the sibling
entities includes a nested child matrix having an entry point; and
the loading of the sibling entities into the nodes includes loading
the nested child matrix into one or more of the nodes, creating a
pointer to the entry point, and storing the pointer in the rollup
matrix.
9. A method for character recognition in an OCR system, the method
comprising: optically scanning a document to obtain data defining
an image; segmenting the image to determine a plurality of sibling
glyphs; each sibling glyph comprising an associated possibility
set, the possibility set consisting of at least one alphanumeric
character candidate information pair, each pair consisting of a
respective candidate and an associated confidence value;
identifying a plurality of parent candidates based on the sibling
glyphs, each parent candidate representing a candidate word;
calculating a parent candidate confidence value for at least some
of the parent candidates; storing the parent candidate confidences
in a rollup matrix in association with the corresponding parent
candidates; and reading from the nodes so as to generate a series
of parent candidate words based on the stored parent candidate
confidence values.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of prior pending U.S.
application Ser. No. 10/410,015 filed Apr. 8, 2003, which is a
continuation of U.S. application Ser. No. 09/528,749 filed Mar. 20,
2000, now issued as U.S. Pat. No. 6,597,809, all of which claim
priority to U.S. provisional application Nos. 60/125,352 filed Mar.
19, 1999 and 60/125,257 filed Mar. 19, 1999 and all are
incorporated herein by this reference.
TECHNICAL FIELD
[0002] The present invention relates to computer-implemented
methods and data structures for producing candidate parent entities
that are ranked in accordance with ranking information associated
with given child entities and, in particular, to such methods for
use with software parsers and data dictionaries, for example, of
the kind utilized in a system for automated reading, validation,
and interpretation of hand print, machine print, and electronic
data streams.
BACKGROUND OF THE INVENTION
[0003] Optical character recognition (OCR) systems and digital
image processing systems are known for use in automatic forms
processing. These systems deal with three kinds of data: physical
data, textual data, and logical data. Physical data may be pixels
on a page or positional information related to those pixels. In
general, physical data is not in a form to be effectively used by a
computer for information processing. Physical data by itself has
neither useable content nor meaning. Textual data is data in
textual form. It may have a physical location associated with it.
It occurs in, for example, ASCII strings. It has content but no
meaning. We know what textual data says, but not what it means.
Logical data has both content and meaning. It often has a name for
what it is.
[0004] For example, there may be region of black pixels in a
certain location on an image. Both the value of the pixels and
their location are physical data. It may be determined that those
pixels, when properly passed through a recognizer, say: "(425)
867-0700." Content has been derived from the physical data to
generate textual data. If we now know that text of this format (or
possibly at this location on a preprinted form) is a telephone
number, the textual data becomes logical data.
[0005] To facilitate reconciliation of imperfections in physical
data and shortcomings of the recognition process, each recognized
element of textual data, e.g., a character, may be represented by a
ranked group of unique candidates called a "possibility set." A
possibility set includes one or more candidate information pairs,
each including a "possibility" and an associated confidence. In the
context of an OCR system, the confidence is typically assigned as
part of the recognition process. For computational efficiency, the
confidences may be assigned within an appropriate base-2 range,
e.g., 0 to 255, or a more compact range, such as 0 to 7. For
example, FIG. 1 shows an enlarged view of an individual glyph 20
that may be physically embodied as a handwritten character or as a
digital pixel image of the handwritten character. From glyph 20, an
optical character recognition process may generate the possibility
set shown in TABLE 1 by assigning possibilities and associated
confidences:
TABLE-US-00001 TABLE 1 possibility confidence c 200 e 123 o 100
[0006] FIG. 2 shows a series of sibling glyphs 22, which are known
as "siblings" because they share the same parent word 24. The
sibling glyphs 22 can be represented by the four possibility sets
as shown in the following TABLE 2:
TABLE-US-00002 TABLE 2 poss conf poss conf poss conf poss conf c
200 h 190 o 100 r 125 o 150 n 100 a 80 n 100 e 100 r 80
The possibilities of these four possibility sets can be readily
combined to form 36 unique strings: "chor", "ohor", "ehor", "cnor",
"cror", etc. The number of unique strings is determined by the
product of the number of character possibilities in each
possibility set, i.e., 3.times.3.times.2.times.2=26.
[0007] To gage or verify their accuracy, the unique "candidate"
strings may be processed by a "dictionary" of valid outcomes. In
the context of OCR, a dictionary is a filter. It has content and
rules. Each candidate string processed by the dictionary is subject
to one of three possible outcomes: it is passed, it is rejected, or
it is modified into a similar string that passes. One example of a
dictionary is based on the English language. For parent word 24 of
FIG. 2, the candidate strings "chor" and "ehar" would be rejected
by such a dictionary, while "char" would be passed.
[0008] Because dictionaries often have a very large amount of
content against which a candidate string is compared, it may be
unduly time-consuming to apply the dictionary to all possible
strings. To improve efficiency, it is desirable, before applying a
dictionary, to rank the candidate strings in order of some
confidence based on the accuracy of recognition. In this way the
candidate strings having the highest confidence of having been
accurately recognized are processed by the dictionary first. Rules
can then be used to determine when to stop dictionary processing,
e.g., when enough candidate strings have been processed to have
isolated the best candidate strings (with a certain probability). A
convenient way to rank candidate strings is to calculate string
confidences based on the confidences of the component character
possibilities that make up each candidate string. A set of
candidate strings and their associated string confidences is
referred to as an "alt-set."
[0009] One way to rank parent candidates for creating an alt-set is
to add the child confidences for each parent candidate. In the
above example, "chor" would have a ranking of 615 (the sum of the
confidences associated with the individual characters c-h-o-r),
"ohor" would have a ranking of 565, "ehor" would have a ranking of
515, etc. Combining the possibility sets to form the 36 unique
strings and to calculate their rankings is simple in this example.
However, there is no obvious way to read the strings out in ranked
order. The strings must first be assigned a ranking, then ordered
or sorted based on their assigned rank. This ordering or sorting
step becomes especially problematic for longer strings formed from
sibling possibility sets having a greater number of possibilities.
By way of illustration, a hypothetical 10-character parent word in
which each child possibility set includes 10 possibilities would
result in 10 billion unique strings. It would be a very
time-consuming and computationally expensive task to rank and order
10 billion 10-character strings.
[0010] Another known way of improving the efficiency of
dictionaries is to use specialized dictionaries that contain
smaller amounts of content than a more generalized dictionary but
that are limited in their application. One such specialized
dictionary is an "n-gram" dictionary, which includes information
about the frequency in which certain character sequences (e.g.,
two-letter, three-letter, etc.) occur in the English language. For
example, the two-letter combination "Qu" (a 2-gram) occurs in
English words much more frequently than "Qo." To benefit from an
n-gram dictionary, the confidence assigned to an n-gram is some
combination of (1) the aggregate character confidences and (2) the
n-gram frequency provided by the n-gram dictionary. Thus,
recognition may have produced Oueen and Queen where the first
character has the possibility set: poss=O, conf=200; poss=Q,
conf=100, but in the English language "Qu" happens much more often
than "Ou", so the 2-gram dictionary would help determine that Queen
is the more likely parent string.
[0011] A need exists for a method of generating candidate strings
in ranked order on an as-needed basis and, more generally, for a
method of generating ranked parent candidates on an on-demand basis
from a series of sibling possibilities. A need also exists for such
a method that can be used with data at different logical levels in
a logical data hierarchy, such as n-grams, words, and phrases.
SUMMARY OF THE INVENTION
[0012] In accordance with the present invention, methods of
organizing a series of sibling data entities are provided for
preserving sibling ranking information associated with the sibling
data entities and for attaching the sibling ranking information to
a joint parent of the sibling data entities to facilitate on-demand
generation of ranked parent candidates. A rollup function of the
present invention builds a rollup matrix containing information
about the sibling entities and the sibling ranking information and
provides a method for reading out the ranked parent candidates from
the rollup matrix in order of their parent confidences, which are
based on the sibling ranking information. Parent confidences may
also be based, in part, on n-gram ranking or other ranking
information.
[0013] External to the rollup function of the present invention,
sibling entities are generated and passed to the rollup function
for processing. Generation of a series of sibling entities may, in
the context of OCR, involve optical scanning, recognition
processing, and parsing. Each sibling entity comprises one or more
ranked child possibilities, each having an associated child
confidence. The number of child possibilities in a sibling entity
is referred to as the "child population" of the sibling entity.
Each sibling entity may include a range of child confidences, one
of which is the maximum child confidence.
[0014] In one aspect of the invention the rollup function is
implemented in computer software operable on a digital computer.
The rollup matrix is modeled as a three-dimensional data array
called a rollup table. The rollup table serves as a convenient
visual aid to understanding the nature of the rollup matrix and
operation of the rollup function. What is the matrix? It should be
understood that nothing in the foregoing description of the rollup
table should be construed as limiting the scope of the invention to
implementation of the rollup matrix in data arrays. Other data
structures, such as linked lists, are also suitable for
implementing the rollup function of the present invention. It
should be understood, therefore, that the term "rollup matrix" as
used herein shall mean data tables, linked lists, and any other
device for defining relationships between nodes in a data
structure, where such nodes include one or more elements of data
and one or more relationships to other nodes, procedures, or nested
rollup functions. Furthermore, it will be apparent from the
foregoing description of the invention that while the invention is
suitable for use with OCR technology, it is also suitable for use
with processing of other types of content-bearing data in which
uncertainty in the data content is sought to be resolved. Non-OCR
applications of the invention involving resolution of empirical
uncertainty may include, for example, bioinformatics systems for
analyzing gene sequencing information.
[0015] After receiving a series of sibling data entities, a matrix
initialization routine of the rollup function establishes a rollup
table and sizes it based on properties of the sibling entities. In
particular, the rollup table is sized to include a series of
"columns" equal in number to the number of sibling entities
received. The dimension of the rollup table spanned by the columns
is referred to as the "width" of the table. The rollup table is
sized in a "height" dimension based on a number of "rows," with
each having a row position indicating its position along the height
dimension of the data table. The number of rows, and consequently
the height of the table, is based on the sum of the maximum child
confidences of the sibling entities. In practice, the number of
rows may be established as equal to the sum of the maximum child
confidences plus one. The rollup table is sized in a "depth"
dimension based on the largest of the child populations of the
sibling entities. The rollup table is a collection of "nodes," each
located in the rollup table at a position defined by column, row
position, and a depth position in the depth dimension.
[0016] Once the rollup function has established the rollup table, a
loading routine of the rollup function then loads the sibling
entities into the rollup table in a predetermined loading sequence
beginning with loading a first sibling entity in a first column of
the series of columns. Each sibling entity is loaded in sequence,
from the first sibling entity to the last sibling entity in the
series. If the sibling entities have no serial relationship, then
an arbitrary, but ordered sequence of loading is chosen. Each child
possibility of the first sibling entity is loaded into a node of
the rollup table located at the first column and at the row having
a row position corresponding to the child confidence of the child
possibility being loaded. The rollup function then proceeds to load
the second sibling entity in the series in a second column. For the
second and each subsequent sibling entity and column, the rollup
function loads each child possibility in one row of the current
column for each row of the immediately preceding column having a
filled node. The child possibilities of the second sibling entity
are loaded in rows of the second column that have row positions
offset from the row positions of filled nodes of the immediately
preceding column (i.e., the first column) by an offset amount
corresponding to the child confidence of the child possibility
being loaded in the second column. The child possibilities of the
third sibling entity are loaded in rows of the third column having
row positions offset from the row positions of filled nodes of the
second column by an offset amount corresponding to the child
confidence of the child possibility being loaded in the third
column, and so on, until the last sibling entity has been loaded in
the last column of the rollup table. Each entry in the last column
of the rollup table is a terminal element. Due to different
confidence values that may be associated with multiple child
possibilities of each of the sibling entities, the loading sequence
may result in the loading of multiple elements in a particular
column and row position. During loading, if a node has already been
filled with a child possibility, the loading routine offsets in the
depth of the rollup table until it reaches an unoccupied node, then
fills that node.
[0017] Upon completion of the loading sequence, another aspect of
the invention involves a roll-out routine of the rollup function,
which may be used to read parent candidates from the rollup table
according to their parent confidences. The reading of parent
candidates, known as "roll-out," begins with a terminal element
known as an entry point. Each parent candidate is assembled in a
sequence opposite the sequence in which the rollup table was
loaded, as follows: After reading a terminal element from the last
column, the roll-out routine then reads a next-to-last element from
the node located at a next-to-last column immediately preceding the
last column and at a row position less than the row position of the
entry point by an amount equal to the child confidence associated
with the terminal element. The next-to-last element is then
prepended to the terminal element to form a string tail. A prefix
element is read from a node located in the column immediately
preceding the next-to-last column and at a row position less than
the node of the next-to-last element by an amount equal to the
confidence of the next-to-last element. The prefix element is then
prepended to the string tail. If the sibling entities forming the
rollup table have no serial relationship, then prepending involves
combining the elements in reverse order of their loading in the
rollup table. This reading process is repeated until the roll-out
routine reaches the first column, completing roll-out of the parent
candidate. If more than one element is located at a particular
column and row location (i.e., elements are stored at more than one
depth position), then the roll-out routine will continue reading
parent candidates beginning from the same entry point until
elements at all occupied nodes at all depths in the appropriate
columns and rows have been read and all parent candidates having
the same parent confidence have been rolled out, or until the
desired number of parent candidates have been rolled out. The
roll-out process is merely repeated for further parent
candidates.
[0018] The method of loading the data table dictates that each row
position corresponds to the parent rank of each parent candidate
assembled from a terminal element located at that row position. The
parent candidate (or candidates) with the greatest parent
confidence may be read from the rollup matrix by beginning at a
maximal node located at the last column and at the row of greatest
row position. Consequently, parent candidates may be read in
decreasing order of parent rank by merely assembling parent
candidates in sequence, beginning with terminal element(s) located
at the maximal node and continuing to read from the rollup table at
entry points of decreasing row position until all parent candidates
have been assembled. The process of building a rollup matrix and
rolling-out parent candidates to form alt-sets can be repeated at
each level in the data hierarchy. If desired, rollup functions can
be nested by storing a nested "child" rollup function pointer at a
node of a parent roll-up table.
[0019] Given the foregoing description of the invention, the use of
software counters to facilitate the loading of the rollup matrix
and the roll-out of parent candidates will be understood by those
skilled in the art.
[0020] In another aspect of the invention, the rollup matrix is
established in a computer memory using a plurality of memory
pointers in place of the 3-dimensional data array of the rollup
table. In this aspect of the invention, the terms "rows" and
"columns" are arbitrary but are used herein to denote memory
locations within the rollup matrix. In reality, each node of the
rollup matrix includes a pointer to other nodes which contain a
child possibility of an adjacent sibling entity. If a node must
point to more than one child possibility, as in the case of
multiple child possibilities at a particular column and row
position, the node will include multiple pointers. When these
multi-pointer nodes are encountered by the roll-out routine, a
branch is indicated so that all pointers of each node are followed
before moving to the next entry point.
[0021] Nodes occupying entry points shall be referred to as "entry
nodes." Entry nodes further include a parent confidence which the
roll-out routine recognizes as assigned to the parent candidate
assembled beginning with the entry node. Entry nodes may also
include a pointer to the next entry node in the matrix, which may
have the same parent confidence or a lesser parent confidence.
Nodes in the "first column," loaded with a child possibility of the
first possibility set, may include a return pointer that may direct
the roll-out routine to output the completed parent candidate for
verification (e.g., using a dictionary) or to proceed to the next
entry node for generation of the next parent candidate. Nodes at
any location in the rollup matrix may also include a pointer to an
entry node of a nested rollup matrix.
[0022] In yet another aspect of the invention, n-gram possibility
sets are generated using a n-gram rollup function in accordance
with the present invention. Comparison of parent candidate n-grams
against an n-gram dictionary allows n-gram candidates to be
weighted in accordance with their relative frequencies of
occurrence in the context of, for example, the English language.
Possibility sets including n-grams are readily accommodated in
establishing the rollup matrix. For 3-grams, the nodes are loaded
with the 3-grams at a row position which is the aggregate of the
confidence of the central character (of the 3-gram) and the
dictionary-provided frequency of the 3-gram. In this aspect of the
invention, child possibilities in the first and last columns of the
rollup matrix must be prepended and appended, respectively, with
nulls (or spaces) so that all child possibilities are 3-grams.
Further, the 3-gram child possibilities must be loaded in the
rollup matrix so that when the parent candidates are rolled-out,
all adjacent 3-grams assembled in a parent candidate share two
characters. For example, "out" in the first column will fit with
"uts" in the second column, but not with "nts."
[0023] In the context of OCR, the rollup function of the present
invention is useful at every level of textual hierarchy. Rollup
functions also avoid fatal problems often encountered by prior art
string generators, which create strings from a series of
possibility sets. Existing string generators suffer from three
major problems. First, they are combinatorically expensive in
memory use-needing a place in memory for each possible string.
Second, string generators must trim strings before generating all
possible strings because of limited space to store the
combinatorically-many strings. Therefore, it is possible for string
generators to result in higher-confidence strings being abandoned
while lower-confidence strings are preserved. Third, string
generators do not guarantee that strings of the same confidence,
once ordered, retain that order.
[0024] The present invention gets around all these problems in a
natural way. First, the rollup function is only geometrically
expensive of memory, not combinatorically. Tables generated by
prior art systems grow as L.times.n.sup.L, where n is the number of
possibilities per possibility set and L is the number of
possibility sets (i.e., the string length). There are n.sup.L
strings of length L that can be generated. By comparison, the
rollup matrix of the present invention grows as
2.times.CF.sub.max.times.L.sup.2, where CF.sub.max is the highest
confidence value in any possibility set. A significant savings over
prior art systems. For L=10, n=3, and CF.sub.max=20, and allowing 1
byte per ASCII character, approximately 590,490 bytes would be
required for ranking tables of prior art systems; while only 12,000
bytes are required for the rollup matrix--a savings of 98%. Second,
candidate strings can be read out of a rollup table in their
decreasing order of confidence without having to store unneeded
strings in memory, while never skipping a higher-confidence parent
candidate for a lower confidence one. The rollup matrix does not
change size with the number of generated strings. Therefore, all
strings are preserved and there is no trimming of strings ever
required. Third, no reordering of parent strings ever takes place
because the rollup matrix is unchanging. Consequently, strings of
the same confidence remain in their original order.
[0025] Parent candidates can be read from the rollup matrix in
decreasing or increasing order of parent confidence. First, a
parent candidate having a desired confidence value can easily be
selected from the matrix by a confidence stored in association with
an entry node of the parent candidate. Parent candidates having
lesser (or greater) confidences can then be read until a desired
lesser (or greater) confidence level is reached. This process can
be repeated until a predetermined number of parent candidates have
been obtained or until all possible parent candidates have been
rolled-out. The rollup function can be interrupted while reading
out a parent candidate to handle some other process, such as
verifying the most recently rolled-out parent candidate using a
dictionary. The rollup function easily returns to where it left off
in the rollup matrix to read out the next-ranked parent candidate
by returning to the location in the rollup matrix that was being
accessed when the interruption occurred. The rollup function of the
present invention provides the above-described benefits without
requiring the production of all of the parent candidates before
subsequent ranking. If a particular child possibility occurs with
at most one confidence value in a possibility set, then the last
rolled-out string is the pointer structure. Even in the case of
allowed duplication, returning to the rollup function is as simple
as storing a pointer to the next entry point in the rollup matrix
and storing a pointer to each position of the table, which may be
accomplished by freezing the internal pointer structure.
[0026] The rollup function of the present invention is, of course,
not limited to strings. Any parent entity can receive
rollup-produced alt-sets from its child entities. For example, gene
sequence information prepared from a human, an animal, a plant, or
any other living organism may be parsed into its nucleotides, each
of which may be represented by an alt-set. Sibling nucleotide
alt-sets can then be loaded into a rollup matrix for the parent
gene. In this way, the frequency of naturally-occurring nucleotide
and coding sequence variations can easily be represented by the
child confidences associated with child possibilities of each
alt-set. Inaccuracies inherent in the gene sequencing process can
be similarly represented by the child confidences.
[0027] Additional aspects and advantages of this invention will be
apparent from the following detailed description of preferred
embodiments thereof, which proceeds with reference to the
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0028] FIG. 1 is an enlarged view of a hand printed glyph;
[0029] FIG. 2 is an enlarged view of a series of sibling
glyphs;
[0030] FIG. 3 is a flow diagram depicting an OCR process for
scanning, parsing, and recognizing handwritten data to create
possibility sets for use with a data verification routine of the
present invention;
[0031] FIG. 4 is a flow diagram showing detail of the data
verification routine of FIG. 3 including a rollup function and
dictionary routine in accordance with a preferred embodiment of the
present invention;
[0032] FIG. 5 is a pictorial view of a three-dimensional data array
in accordance with a first preferred embodiment of the present
invention;
[0033] FIGS. 6A, 6B, 6C, and 6D are two-dimensional pictorial views
of a rollup matrix in accordance with the present invention showing
a loading sequence for loading the alt-sets of Table 3 into the
rollup matrix;
[0034] FIG. 7 is an exploded three-dimensional view of the loaded
rollup matrix of FIG. 6D;
[0035] FIGS. 8A, 8B, 8C, and 8D are show a sequence of rolling out
a parent candidate from the loaded rollup matrix of FIG. 6D;
[0036] FIG. 9 is a diagram of an alternative embodiment of the
rollup matrix of FIG. 6D including a linked list implemented in a
computer memory;
[0037] FIG. 10 is a flow diagram showing steps taken in preparation
and validation of n-gram alt-sets for loading in a rollout matrix
for a parent string of the n-grams;
[0038] FIG. 11 is a two-dimensional pictorial view showing nested
rollup matrices;
[0039] FIG. 12 is a flow diagram showing steps for establishing and
loading of the nested rollup matrices of FIG. 11; and
[0040] FIG. 13 is flow diagram showing parent candidates being
rolled out from the nested rollup matrices of FIG. 11.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0041] FIG. 3 is a flow diagram of an OCR process 30 in accordance
with a first preferred embodiment of the present invention. With
reference to FIG. 3, a document 32 bearing physical textual data is
scanned using an optical scanner 34, which produces a digital pixel
image of the physical data on document 32. A segmentation process
36 of the OCR process 30 receives the pixel image from the optical
scanner and segments the pixel image into data segments for
processing by a recognizer 38. Recognizer 38 analyzes the data
segments to produce a possibility set ("pos-set") for each data
segment. Empirical uncertainty in the physical data and
inaccuracies of the scanning, segmentation and recognition process
are represented in the pos-sets by including multiple child
possibilities in each pos-set and by assigning child confidences to
the child possibilities. For example, recognizer 38 separates a
parent string (as in the parent word 24 of FIG. 2) into its sibling
glyphs and outputs a pos-set for each glyph. The pos-sets are
output to a data verification routine 40, which uses a rollup
function 60 (FIG. 4) and possibly one or more dictionaries 150
(FIG. 4) in accordance with the present invention.
[0042] FIG. 4 is a flow diagram of rollup function 60 of data
verification routine 40 (FIG. 3). With reference to FIG. 4, a
matrix initialization routine 62 of rollup function 60, receives
pos-sets 64 from recognizer 38. FIG. 5 is a pictorial view of a
three-dimensional data array 66, which represents a data matrix in
accordance with the present invention. Data array 66, includes rows
70, columns 72, and tiers 74 that together form nodes 76. With
reference to FIGS. 4 and 5, matrix initialization routine
establishes a size of data array 66 based on pos-sets 64. For
purposes of a simple illustration, TABLE 3 presents four sibling
pos-sets.
TABLE-US-00003 TABLE 3 poss conf poss conf poss conf poss conf a 2
n 1 t 1 s 1 o 1 u 0 5 0
A first pos-set shown in TABLE 3 includes two child possibilities,
"a" and "o", which are assigned child confidences 2 and 1,
respectively. A second pos-set includes child possibilities n and
u, having associated child confidences 1 and 0, respectively. And
so on. The matrix initialization routine calculates a sum of the
maximum confidences of the four pos-sets (2+1+1+1=5) and adds one
(5+1=6) to establish a height 80 of data array 66. Data array 66,
thus, includes six rows 70, having row heights R0, R1, R2, R3, R4,
and R5. A width 82 of data array 66 is equal to the number of
pos-sets 64. A depth 84 of data array 66 is equal to the largest
number of child possibilities in any of the pos-sets 64. In this
example, three of the pos-sets are equally large, having two child
possibilities.
[0043] Once data array 66 has been established and sized, a loading
routine 90 of rollup function 60 loads pos-sets 64 into data array
66. FIGS. 6A, 6B, 6C, and 6D depict a loading sequence followed by
loading routine 90. With reference to FIG. 6A, a data table 92
provides a two-dimensional representation of the three-dimensional
data array 66 of FIG. 5, including four columns C1, C2, C3, and C4,
each of which is divided by broken lines to indicate tiers 74 of
data array 66 (FIG. 5). Loading routine 90 loads the child
possibilities 94 of the first pos-set into the first column C1 so
that each child possibility 94 is loaded in a node 96 at a row
position equal to the child confidence 98 corresponding the child
possibility 94. Thus, child possibility "o", which has an
associated child confidence of one is loaded at the node located at
row R1, and child possibility "a" is loaded at row R2 because it
has an associated child confidence of two.
[0044] When loading routine 90 completes loading of the first
pos-set (FIG. 6A), it proceeds to load the second pos-set into data
table 92. With reference to FIG. 6B, each child possibility of the
second pos-set is loaded in one node 96 of the second column (C2)
for each row of the first column (C1) having filled nodes, but at a
row height greater than the row height of the filled nodes 96 of
column C1 by an amount equal to the child confidences being loaded.
Thus, child possibility "u" having a child confidence of zero is
loaded in nodes located at rows R1 and R2 of column C2, since rows
R1 and R2 are filled in column C1. Child possibility "n" is loaded
in nodes located at rows R2 and R3 of column C2, which are greater
than the row positions of the filled nodes (R1 and R2) of column C1
by an amount equal to the child confidence (one) associated with
child possibility "n." Because the node located at C2, R2, TO, is
already filled with child possibility "u", loading routine 90 loads
child possibility n at node C2, R2, Ti so that no more than one
child possibility is loaded in each node.
[0045] Loading routine 90 then continues to load successive
pos-sets 64 in sequence in successive columns, as depicted in FIGS.
6C and 6D, until all pos-sets 64 have been loaded in data table 92.
As in column C2, child possibilities 94 are loaded in nodes 96
located at row positions that are greater (by an amount equal to
the child confidence of the child possibility being loaded) than
the row position(s) of rows of the immediately preceding column
that have filled nodes. Nodes of the last column (C4) that are
loaded with child possibilities contain data entities that are
known as terminal elements 100.
[0046] FIG. 7 is an exploded view of the loaded data table 92 of
FIG. 6D showing its loaded data in a three-dimensional
representation in accordance with three-dimensional data array 66
of FIG. 5.
[0047] To extract parent candidate strings from data table 92, a
roll-out routine 110 of rollup function 60 is provided (FIG. 4).
FIG. 8A depicts the steps taken by roll-out routine 110, in rolling
out parent candidate "ants", i.e., the parent candidate comprising
the sibling characters "a", "n", "t", and "s". Parent candidate
"ants" has the greatest aggregate confidence of any of the parent
candidates because its terminal element ("s") 100 is located in the
row of data table 92 having the greatest row position (R5), i.e., a
maximal terminal element 112. With reference to FIG. 8A, roll-out
routine 110 reads from columns C4, C3, C2, and C1, in the order
opposite to which the columns were loaded. Terminal element "s" 100
(which is also the maximal terminal element 112) is read initially.
Next, roll-out routine 110 reads next-to-last child element "t" 116
from the immediately previous column (C3) and from row R4, which
has a row position less than the row position of terminal element
"s" by the amount of the child confidence associated with terminal
element "s" (i.e. one). Roll-out routine 110 prepends next-to-last
child element "t" to the terminal element "s" to form a string tail
of "ts." The child confidence of one associated with next-to-last
child element "t" 116 then directs roll-out routine to read prefix
element "n" 118 from row R3, column C2 (because row R3 has a row
position one less than the row position of R4). Roll-out routine
110 prepends prefix element "n" 118 to the string tail "ts", to
form the partial string "nts." Element "a" 120, is then read
because it is loaded in row R2, which is one less (the child
confidence associated with prefix element "n" 118) than the row
position of prefix element "n" 118. Element "a" 120 is prepended to
complete the formation of candidate parent string "ants". The
parent confidence associated with "ants" is equal to five, which is
the row position of the terminal element 100a used to extract
"ants".
[0048] FIG. 8B depicts the steps taken by roll-out routine 110, in
rolling out parent candidate "ant5". With reference to FIG. 8B,
terminal element "5" has an associated child confidence of zero,
which directs roll-out routine to read next-to-last element "t"
from the same row position (R4) in column C3. The parent confidence
associated with "ant5" is equal to four, which is the row position
of terminal element "5" 100b used to extract "ant5".
[0049] FIGS. 8C and 8D depict the steps taken by roll-out routine
110, in rolling out respective parent candidates "auts" and "onts."
Because there are two entries in row R2, column C2, roll-out
routine 110 rolls out two unique parent candidates ending with
terminal element "s" 100c, both having an associated parent
confidence of four, which is equal to the row height of row R4,
where terminal element "s" 100c is located.
[0050] In accordance with an alternative embodiment of the present
invention, FIG. 9 shows the loaded data table 92 of FIGS. 6D and 7
embodied as a linked-list rollup matrix 126. With reference to FIG.
9, rollup matrix 126 includes a pointer structure 128 to nodes 96.
To roll-out the parent candidate "ants", roll-out routine 110
starts at an initial entry point 130 that includes terminal element
100a (element "s" of maximal terminal element 112). Roll-out
routine 110 then reads out elements "t" 116, "n" 118, and "a" 120
by following respective pointers 134, 136, and 138 and prepends
them to element "s" 100a. A return pointer 140 indicates to
roll-out routine 110 that it has completed construction of the
parent candidate. A parent confidence 141 of the parent candidate
"ants" is stored in association with the terminal element "s" 100a.
All terminal elements of rollup matrix 126 serve as entry points
142 for rolling out one or more parent candidates. As in the
roll-out sequences shown in FIGS. 8C and 8D, two parent candidates
can be rolled out of rollup matrix 126 by beginning with terminal
element "s" 100c. A branch node 144 of rollup matrix 126 includes
two pointers 146, 148, which indicate to roll-out routine 110 that
two different parent candidates use branch node 144 and that
roll-out routine 110 needs to execute a branch at branch node 144.
Those skilled in the art will understand that more than one branch
node may clearly exist in rollup matrix, and that some branch nodes
will have more than two pointers (if the matrix is "deeper" than 2
tiers).
[0051] After rolling out of each parent candidate (typically in
decreasing order of parent confidence), rollup function may output
each parent candidate to a dictionary routine 150 (FIG. 4) for
validation using an appropriate parser and dictionary. One
embodiment of handling dictionary processing is shown in FIG. 4,
and includes conditional iteration of roll-out routine 110. An
iteration step 154 is conditional upon whether the parent candidate
output by roll-out routine 110 passes the dictionary test (160)
and, if it does, whether some other stop limit 170 has been met.
For example stop limit 170 may trigger OCR process 30 (FIG. 3) to
terminate verification of the parent element represented by rollup
matrix 126 (and rollup table 92), and to load the next series of
pos-sets scanned and recognized from document 32.
[0052] FIG. 10 is a flow diagram showing steps taken in preparation
and validation of n-gram alt-sets for loading in a rollout matrix
for a parent string of the n-grams. With reference to FIG. 10, an
n-gram verification process 200 receives pos-sets from OCR system
(step 210) and assembles them in computer memory to form a ranked
list of n-gram candidates (step 212). N-gram candidates within a
single ranked list may have different lengths, for example when one
of the pos-sets includes both an "m" possibility and an "rn"
possibility. To accommodate n-gram candidates having different
lengths, a length gage routine 214 of n-gram verification process
200 determines the length of each n-gram candidate. The n-gram
candidates are then processed by an appropriate n-gram dictionary
216. N-gram dictionary 216 is a specialized dictionary or
collection of specialized dictionaries that includes information
about frequency of occurrence of n-grams (for example 2-grams,
3-grams, etc.) in written language or some subset of written
language. N-gram dictionary 216 assigns an n-gram confidence to
each n-gram candidate based on (i) the dictionary frequency rating
for the n-gram and (ii) a child confidence associated with a
central character of the n-gram candidate. N-gram and its
associated n-gram confidence are then appended to an n-gram alt-set
(step 218). Steps 214, 216, and 218 are then repeated until all of
the lists of n-gram parent candidates have been processed through
the dictionary and output as n-gram alt-sets. After all n-gram
alt-sets have been completed, a string-sized rollup matrix is built
using the alt-sets as sibling entities (step 220). Parent string
candidates can then be rolled out of string-sized rollup matrix in
ranked order (step 222) and processed using a string dictionary
(step 224) before outputting ranked parent strings (step 226).
[0053] FIG. 11 is a two-dimensional pictorial view showing nested
rollup matrices 240 established in accordance with the present
invention. With reference to FIG. 11, nested rollup matrices 240
include a child rollup matrix 250 nested within a parent rollup
matrix 260. Child rollup matrix 250 is said to be "nested" because
complete candidates that may be rolled out of child rollup matrix
250 are referenced by pointers within parent rollup matrix 260. In
this example, child rollup matrix 250 represents candidate city
names in a typical rollup matrix in accordance with the present
invention. However, any child entity can be represented in a nested
child rollup matrix. Parent rollup matrix 260 is a typical rollup
matrix in accordance with the present invention. In this example,
parent rollup matrix 260 includes sibling city, state, and zip-code
alt-sets. First and second city nodes 262, 264 of parent rollup
matrix 260 include respective first and second city pointers 266,
268 to respective first and second entry points 270, 272 of child
rollup matrix 250. First and second entry points 270, 272 are
terminal nodes of child rollup matrix 250 having associated city
confidences 274, 276. While the nested rollup matrices 240 of FIG.
11 include only one nested child matrix, it would be
straightforward to nest multiple child matrices within a single
parent rollup matrix. Likewise, it would be simple to create a
hierarchy of nested rollup matrices including three or more layers
of rollup matrices, rather than the two layers (child rollup matrix
250 and parent rollup matrix 260) of FIG. 11.
[0054] In setting up nested rollup matrices 240, child rollup
matrix 250 is established before establishing parent rollup matrix
260. This order of establishing nested rollup matrices 240 insures
that city confidences 274, 276 of child rollup matrix 250 may be
taken into account when establishing, sizing, and loading parent
rollup matrix 260. When loading first and second city pointers 266,
268 in parent rollup matrix 260, city confidences 274, 276 of child
rollup matrix 250 determine how parent rollup matrix 260 is
loaded.
[0055] FIG. 12 is a flow diagram showing steps for establishing and
loading of the nested rollup matrices of FIG. 11. With reference to
FIG. 12, a child rollup matrix is first established and loaded
(step 300). Once loaded, entry points for child candidates of the
child rollup matrix, and their associated child confidences are
available. These child candidates, entry points, and child
confidences are then taken into account in establishing and sizing
parent rollup matrix (step 310). Parent rollup matrix is then
loaded (step 320). In the example of FIG. 11, parent rollup matrix
260 is loaded with a zip-code (postal code) alt-set in its terminal
column and a state alt-set in its next-to-last column. Parent
rollup matrix is also loaded with city pointers 266, 268 to
appropriate entry points 270, 272 of child rollup matrix 250. After
parent rollup matrix has been loaded (step 320), ranked parent
candidates may then be rolled out (step 330) for processing by a
dictionary. The dictionary required for use with the nested rollup
matrices 240 shown in the example of FIG. 11 would be a
city-state-zip dictionary for verifying specific city-state-zip
combinations.
[0056] FIG. 13 is flow diagram showing a sequence of steps for
rolling out a parent candidate from the nested rollup matrices 240
of FIG. 11. With reference to FIG. 13, a nested roll-out routine
400 starts at an entry point, which is a terminal parent node of a
linked list of parent matrix (step 410). All subsequent steps shown
in FIG. 13 are identical regardless of whether the current node is
a terminal node or another node of nested rollup matrices 240.
Nested roll-out routine 400 next determines whether the parent node
includes a pointer to a nested child matrix (step 420). If not,
then nested roll-out routine 400 reads the element stored in the
current node (step 430) and prepends it to a parent candidate tail.
Nested roll-out routine 400, then determines whether the node
includes a return pointer that would indicate completion of the
parent candidate (step 440). If not, then nested roll-out routine
advances to the next node in the linked list (step 450) and returns
to step 420. If a parent node includes a nested matrix pointer to a
nested rollup matrix (at step 410) then nested roll-out routine 400
proceeds to store in memory an address of the parent node that
includes the nested matrix pointer (step 460). Nested roll-out
routine 400, then rolls out a child candidate from the nested child
matrix (step 470), prepends the child candidate to the parent
candidate tail (step 480). Nested roll-out routine then restores
the address of the last-read parent node, which was previously
stored in memory and returns to the parent rollup function (step
490), continuing on at the last read parent node.
[0057] When a parent node includes a return pointer (step 440),
nested roll-out routine completes its assembly of parent candidate
and processes it using dictionary process 500. If the parent
candidate passes the dictionary test, it is output. The nested
roll-out function can be repeated for each terminal node of parent
roll-out matrix to complete roll out of all parent candidates.
[0058] It will be obvious to those having skill in the art that
many changes may be made to the details of the above-described
embodiments of this invention without departing from the underlying
principles thereof. The scope of the present invention should,
therefore, be determined only by the following claims.
* * * * *