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Patent US5333209A successfully refreshed from Google Patents.
US5333209A
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Method of recognizing handwritten symbols
Assignee: Nokia Bell Labs
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Filing: 1992-03-24
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Granted: 1994-07-26
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Expires: 2012-03-24
Abstract
Abstract An unknown handwritten symbol written on a digitizing tablet is compared with symbols in a predefined "alphabet" or library of model symbols and the closest match chosen. Recognition is independent of the size, position or orientation of the symbols. The alphabet can be any collection of symbols, such as alphanumeric characters, ideograms or words in cursive script and is created by writing at least one example of each symbol on the tablet. A sequence of samples of the pen position is recorded while a symbol is being written. The samples form a vector, which is then translated so that the centroid of the symbol lies at an origin. The comparison, which can easily be done in real time, involves calculating a correlation factor from scalar products of the vector for the unknown symbol and two versions of the vector for each model symbol and choosing the model symbol having the highest correlation factor. If needed to distinguish between model symbols with similar correlation factors, the comparison can also include calculating a rotation factor from such vectors for use in making such choice. Embodiments of the invention can be configured that are user-independent, user-dependent or that evolve from one to the other.
Inventors
- 👤 Frank W. Sinden
- 👤 Gordon T. Wilfong
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-29 17:33
CPC Classifications
G; G07; G07C; G07C9/00; G07C9/30; G07C9/32; G07C9/35; G06; G06V; G06V30/00; G06V30/10; G06V30/32; G06V30/333; G06V30/36; G06V30/373
Claims Analysis
Total Claims: 38
Claim 1
Independent Claim
197 words
A machine method of recognizing an unknown symbol as one of a set of stored model symbols wherein said unknown symbol and said model symbols are each represented as an ordered set of coordinate samples, which comprises: translating the samples for said unknown symbol and each model symbol so that the centroids of said unknown symbol and said model symbol lie at a common origin, comparing said unknown symbol with each model symbol in turn by equalizing the number of translated samples for said unknown symbol and said model symbol and determining the correlation of said unknown symbol with said model symbol from the equalized number of said translated samples for said unknown symbol and the equalized number of said translated samples for said model symbol by calculating said correlation as a quantity indicating the closeness of a representation of all possible rotations and sizes of said unknown symbol to a representation of all possible rotations and sizes of said model symbol and recognizing said unknown symbol as the model symbol associated with the highest correlation, whereby said recognition is independent of the size, position or orientation of said unknown symbol with respect to said model symbols.
Claim 2
Dependent Claim
197 words
A machine method of recognizing an unknown symbol as one of a set of stored model symbols wherein said unknown symbol and said model symbols are each represented as an ordered set of coordinate samples, which comprises: translating the samples for said unknown symbol and each model symbol so that the centroids of said unknown symbol and said model symbol lie at a common origin, comparing said unknown symbol with each model symbol in turn by equalizing the number of translated samples for said unknown symbol and said model symbol and determining the correlation of said unknown symbol with said model symbol from the equalized number of said translated samples for said unknown symbol and the equalized number of said translated samples for said model symbol by calculating said correlation as a quantity indicating the closeness of a representation of all possible rotations and sizes of said unknown symbol to a representation of all possible rotations and sizes of said model symbol and recognizing said unknown symbol as the model symbol associated with the highest correlation, whereby said recognition is independent of the size, position or orientation of said unknown symbol with respect to said model symbols.
Claim 3
Dependent Claim
81 words
The method of claim 1 wherein said calculating step further comprises: calculating said correlation as: ##EQU9## where vector v.sub.α is the equalized ordered set of translated samples for said unknown symbol, vector v k is the equalized ordered set of translated samples for said model symbol, vector u k is derived from vector v k by a counterclockwise rotation of π/2 radians and said symbol associated with the highest correlation is the model symbol associated with the highest value of C.
Claim 4
Dependent Claim
30 words
The method of claim 1 which further comprises: storing said ordered sets of coordinate samples for said model symbols while said model symbols are being handwritten on a digitizing tablet.
Claim 5
Dependent Claim
52 words
The method of claim 3 which further comprises: storing the number of pen strokes used to write each model symbol and each unknown symbol and wherein said comparing step further comprises: limiting said comparing step to those of said model symbols having the same number of pen strokes as the unknown symbol.
Claim 6
Dependent Claim
18 words
The method of claim 4 wherein said equalizing step further comprises equalizing said samples on a stroke-by-stroke basis.
Claim 7
Dependent Claim
60 words
The method of claim 5 wherein said equalizing step further comprises: limiting vector v.sub.α to a subset of essentially uniformly spaced samples from said ordered set for said unknown symbol, and limiting vector v k to include the same number of essentially uniformly spaced samples, stroke-by-stroke, from said ordered set for said model symbol as in said limited vector v.sub.α.
Claim 8
Dependent Claim
69 words
The method of claim 5 wherein said equalizing step further comprises: augmenting the number of samples in said vector v k by interpolating a fixed number of additional samples between each adjacent pair of actual samples in said vector v k , and limiting vector v k to include the same number of essentially uniformly spaced samples, stroke-by-stroke, from said augmented vector v k as in said vector v.sub.α.
Claim 9
Dependent Claim
56 words
The method of claim 1 which further comprises: if an unknown symbol is recognized incorrectly, adapting said set of model symbols by adding said ordered sequence of samples for said incorrectly recognized symbol to said set of model symbols as an additional representative example of the correct symbol to be associated with said incorrectly recognized symbol.
Claim 10
Dependent Claim
38 words
The method of claim 1 which further comprises: continually adapting said set of model symbols by storing said ordered sequences of samples for each of a specified number of the most recently entered instances of each model symbol.
Claim 11
Dependent Claim
81 words
The method of claim 2 which comprises the additional step of: establishing a threshold value related to said highest value of C, if the value of C for more than one model symbol exceeds said threshold value then, for each model having a value of C above said threshold value, calculating the rotation of said model symbol with respect to said unknown symbol and wherein said recognizing step comprises recognizing said unknown symbol as the model symbol having the smallest rotation.
Claim 12
Dependent Claim
33 words
The model of claim 10 wherein said rotation calculating step further comprises: calculating said rotation as: ##EQU10## wherein said model symbol having the smallest rotation is the symbol having R closest to unity.
Claim 13
Dependent Claim
25 words
The method of claim 11 wherein said establishing step further comprises establishing said threshold value as a fixed percentage of said highest value of C.
Claim 14
Dependent Claim
28 words
The method of claim 3 for use in verifying signatures wherein said model symbols are previously written signatures and said unknown symbol is a signature to be verified.
Claim 15
Dependent Claim
41 words
The method of claim 13 comprising the additional step of: comparing said highest value of C with the next-highest value of C and rejecting said signature to be verified if said next-highest value exceeds a certain percentage of said highest value.
Claim 16
Dependent Claim
37 words
The method of claim 13 comprising the additional step of: comparing said highest value of C with a threshold value and rejecting said signature to be verified if said highest value does not exceed said threshold value.
Claim 17
Dependent Claim
30 words
The method of claim 1 which further comprises: storing sets of pre-recorded coordinate samples for a plurality of versions of said model symbols in said set of stored model symbols.
Claim 18
Dependent Claim
41 words
The method of claim 16 which further comprises: as unknown symbols are recognized, replacing at least one set of pre-recorded samples for a model symbol with at least one set of samples for an unknown symbol recognized as said model symbol.
Claim 19
Dependent Claim
37 words
The method of claim 17 wherein said replacing step further comprises: replacing said sets of pre-recorded samples for a model symbol with a smaller number of sets of samples for unknown symbols recognized as said model symbol.
Claim 20
Dependent Claim
39 words
The method of claim 16 which further comprises: as unknown symbols are recognized, augmenting said sets of pre-recorded samples for a model symbol with at least one set of samples for an unknown symbol recognized as said model symbol.
Claim 21
Dependent Claim
197 words
A machine method of finding the closest match for an unknown symbol in a set of stored model symbols wherein said unknown symbol and said model symbols are each represented as an ordered set of coordinate samples with the centroids of said unknown symbol and said model symbols at a common origin, said samples for said unknown symbol are compared in turn with said samples for each of said model symbols, before each comparison, the number of translated samples for said unknown symbol is equalized with the number of said samples for said model symbol and during each comparison, the correlation of said unknown symbol with said model symbol is calculated from said equalized and translated samples and the model symbol associated with the highest correlation is identified as the closest match CHARACTERIZED IN THAT said correlation calculation step further comprises: calculating said correlation as a quantity indicating the closeness of a representation of all possible rotations and sizes of said unknown symbol to a representation of all possible rotations and sizes of said model symbol, whereby said comparisons are independent of the size, position or orientation of said unknown symbol with respect to said model symbols.
Claim 22
Dependent Claim
197 words
A machine method of finding the closest match for an unknown symbol in a set of stored model symbols wherein said unknown symbol and said model symbols are each represented as an ordered set of coordinate samples with the centroids of said unknown symbol and said model symbols at a common origin, said samples for said unknown symbol are compared in turn with said samples for each of said model symbols, before each comparison, the number of translated samples for said unknown symbol is equalized with the number of said samples for said model symbol and during each comparison, the correlation of said unknown symbol with said model symbol is calculated from said equalized and translated samples and the model symbol associated with the highest correlation is identified as the closest match CHARACTERIZED IN THAT said correlation calculation step further comprises: calculating said correlation as a quantity indicating the closeness of a representation of all possible rotations and sizes of said unknown symbol to a representation of all possible rotations and sizes of said model symbol, whereby said comparisons are independent of the size, position or orientation of said unknown symbol with respect to said model symbols.
Claim 23
Dependent Claim
83 words
The method of claim 20 FURTHER CHARACTERIZED IN THAT said calculating step further comprises calculating said correlation as: ##EQU11## where vector v.sub.α is the equalized ordered set of translated samples for said unknown symbol, vector v k is the equalized ordered set of translated samples for said model symbol, vector u k is derived from vector v k by a counterclockwise rotation of π/2 radians and said symbol associated with the highest correlation is the symbol associated with the highest value of C.
Claim 24
Dependent Claim
35 words
The method of claim 20 FURTHER CHARACTERIZED IN THAT said method further comprises: storing said ordered sets of coordinate samples for said model symbols while said model symbols are being handwritten on a digitizing tablet.
Claim 25
Dependent Claim
57 words
The method of claim 22 FURTHER CHARACTERIZED IN THAT said method further comprises: storing the number of pen strokes used to write each model symbol and each unknown symbol and wherein said comparing step further comprises: limiting said comparing step to those of said model symbols having the same number of pen strokes as the unknown symbol.
Claim 26
Dependent Claim
21 words
The method of claim 23 FURTHER CHARACTERIZED IN THAT said equalizing step further comprises equalizing said samples on a stroke-by-stroke basis.
Claim 27
Dependent Claim
63 words
The method of claim 24 FURTHER CHARACTERIZED IN THAT said equalizing step further comprises: limiting vector v.sub.α to a subset of essentially uniformly spaced samples from said ordered set for said unknown symbol, and limiting vector v k to include the same number of essentially uniformly spaced samples, stroke-by-stroke, from said ordered set for said model symbol as in said limited vector v.sub.α.
Claim 28
Dependent Claim
57 words
The method of claim 20 FURTHER CHARACTERIZED IN THAT if an unknown symbol is recognized incorrectly, adapting said set of model symbols by adding said ordered sequence of samples for said incorrectly recognized symbol to said set of model symbols as an additional representative example of the correct symbol to be associated with said incorrectly recognized symbol.
Claim 29
Dependent Claim
43 words
The method of claim 20 FURTHER CHARACTERIZED IN THAT said method further comprises continually adapting said set of model symbols by storing said ordered sequences of samples for each of a specified number of the most recently entered instances of each model symbol.
Claim 30
Dependent Claim
84 words
The method of claim 21 FURTHER CHARACTERIZED IN THAT said method further comprises: establishing a threshold value related to said highest value of C, if the value of C for more than one model symbol exceed said threshold value then, for each model symbol having a value of C above said threshold value, calculating the rotation of said model symbol with respect to said unknown symbol and wherein said recognizing step comprises recognizing said unknown symbol as the model symbol having the smallest rotation.
Claim 31
Dependent Claim
36 words
The model of claim 28 FURTHER CHARACTERIZED IN THAT said rotation calculating step further comprises: calculating said rotation as: ##EQU12## wherein said model symbol having the smallest rotation is the symbol having R closest to unity.
Claim 32
Dependent Claim
28 words
The method of claim 29 FURTHER CHARACTERIZED IN THAT said establishing step further comprises establishing said threshold value as a fixed percentage of said highest value of C.
Claim 33
Dependent Claim
34 words
The method of claim 22 FURTHER CHARACTERIZED IN THAT said method is used for verifying signatures, said stored model symbols are previously written signatures and said unknown symbol is a signature to be verified.
Claim 34
Dependent Claim
44 words
The method of claim 31 FURTHER CHARACTERIZED IN THAT said method further comprises: comparing said highest value of C with the next-highest value of C and rejecting said signature to be verified if said next-highest value exceeds a certain percentage of said highest value.
Claim 35
Dependent Claim
36 words
The method of claim 20 FURTHER CHARACTERIZED IN THAT said method further comprises: storing sets of pre-recorded coordinate samples for a plurality of versions of each said model symbol in said set of stored model symbols.
Claim 36
Dependent Claim
47 words
The method of claim 33 FURTHER CHARACTERIZED IN THAT said method further comprises: as unknown symbols are recognized, replacing at least part of said pre-recorded samples for each said model symbol with at least one set of samples for an unknown symbol recognized as said model symbol.
Claim 37
Dependent Claim
41 words
The method of claim 34 FURTHER CHARACTERIZED IN THAT said replacing step further comprises: replacing said sets of pre-recorded samples for each said model symbol with a smaller number of sets of samples for unknown symbols recognized as said model symbol.
Claim 38
Dependent Claim
45 words
The method of claim 33 FURTHER CHARACTERIZED IN THAT said method further comprises as unknown symbols are recognized, augmenting said sets of pre-recorded samples for each said model symbol with at least one set of samples for an unknown symbol recognized as said model symbol.
References Cited (Backward Citations)
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1997-07-25 | 1998-01-24 | 1998-07-26 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2001-07-25 | 2002-01-24 | 2002-07-26 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2005-07-25 | 2006-01-24 | 2006-07-26 | $7700.00 | Window Closed / Lapsed |