US5757960A
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Method and system for extracting features from handwritten text
Assignee: Google Technology Holdings LLC
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Filing: 1997-02-28
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Granted: 1998-05-26
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Expires: 2017-02-28
Abstract
Abstract A handwriting recognition system achieves a higher recognition rate by using a feature extraction method which computes features based on multiple data frames. A plurality of data frames is generated from handwritten text received by the system. Each data frame includes samples taken from the handwritten text. Individual-frame features are extracted from individual data frames, and in turn, multi-frame features are extracted from individual-frame features which correspond to different data frames.
Inventors
- 👤 Michael Chase Murdock
- 👤 Shay-Ping Thomas Wang
- 👤 Nicholas Mikulas Labun
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-01 01:37
CPC Classifications
G; G06; G06V; G06V30/00; G06V30/10; G06V30/32; G06V30/333; G06V30/19; G06V30/196; G06V30/36
Claims Analysis
Total Claims: 20
Claim 1
Independent Claim
299 words
In a system for recognizing a plurality of characters from a sample of handwritten text, the system utilizing a classifier which responds to a plurality of features extracted from the sample of handwritten text, a method for extracting the plurality of features, the method comprising the steps of: (a) receiving the sample of handwritten text; (b) sampling the handwritten text, over time, to form a sequence of sample datum; (c) partitioning the sequence of sample datum into a temporal sequence of data frames, each of the temporal sequence of data frames including at least two of the sequence of sample datum; (d) extracting a plurality of individual-frame feature from the temporal sequence of data frames, each of the plurality of individual-frame features having a magnitude and corresponding to one of the temporal sequence of data frames, wherein at least one of the individual-frame features includes a plurality of coefficients of a first order polynomial which is fitted to a curvilinear velocity profile: v.sub.k =a.sub.0 +a.sub.1 v.sub.(k-1) +a.sub.2 v.sub.(k-2) +a.sub.3 v.sub.(k-3) wherein v.sub.(k) represents the curvilinear velocity of a kth sample datum, v.sub.(k-1) represents the curvilinear velocity of a (k-1)th sample datum, v.sub.(k-2) represents the curvilinear velocity of a (k-2)th sample datum, v.sub.(k-3) represents the curvilinear velocity of a (k-3)th sample datum, k is an integer index, and a 0 , a 1 , a 2 , and a 3 represent the coefficients of the first order polynomial; and (e) extracting a multi-frame feature, corresponding to a specific data frame of the temporal sequence of data frames, from one of: at least two of the plurality of individual-frame features, at least two of the temporal sequence of data frames, and at least one of the plurality of individual-frame features and at least one of the temporal sequence of data frames.
Claim 2
Dependent Claim
299 words
In a system for recognizing a plurality of characters from a sample of handwritten text, the system utilizing a classifier which responds to a plurality of features extracted from the sample of handwritten text, a method for extracting the plurality of features, the method comprising the steps of: (a) receiving the sample of handwritten text; (b) sampling the handwritten text, over time, to form a sequence of sample datum; (c) partitioning the sequence of sample datum into a temporal sequence of data frames, each of the temporal sequence of data frames including at least two of the sequence of sample datum; (d) extracting a plurality of individual-frame feature from the temporal sequence of data frames, each of the plurality of individual-frame features having a magnitude and corresponding to one of the temporal sequence of data frames, wherein at least one of the individual-frame features includes a plurality of coefficients of a first order polynomial which is fitted to a curvilinear velocity profile: v.sub.k =a.sub.0 +a.sub.1 v.sub.(k-1) +a.sub.2 v.sub.(k-2) +a.sub.3 v.sub.(k-3) wherein v.sub.(k) represents the curvilinear velocity of a kth sample datum, v.sub.(k-1) represents the curvilinear velocity of a (k-1)th sample datum, v.sub.(k-2) represents the curvilinear velocity of a (k-2)th sample datum, v.sub.(k-3) represents the curvilinear velocity of a (k-3)th sample datum, k is an integer index, and a 0 , a 1 , a 2 , and a 3 represent the coefficients of the first order polynomial; and (e) extracting a multi-frame feature, corresponding to a specific data frame of the temporal sequence of data frames, from one of: at least two of the plurality of individual-frame features, at least two of the temporal sequence of data frames, and at least one of the plurality of individual-frame features and at least one of the temporal sequence of data frames.
Claim 3
Dependent Claim
34 words
The method of claim 1 wherein the plurality of individual-frame features includes a feature selected from the group consisting of angular velocity, curvilinear velocity, stroke angle, stroke curvature, data frame index, and Fourier coefficients.
Claim 4
Dependent Claim
30 words
The method of claim 1 wherein step (d) includes deriving the plurality of individual-frame features from coordinates of the sample of handwritten text, over time, as the sample is written.
Claim 5
Dependent Claim
74 words
The method of claim 1 wherein the step of extracting a multi-frame feature includes determining whether a difference between the magnitude of an individual-frame feature extracted from the specific data frame and the magnitude of an individual-frame feature extracted from a data frame adjacent to the specific data frame is less than a threshold value: (i) if so, setting the multi-frame feature to 1; and (ii) if not, setting the multi-frame feature to 0.
Claim 6
Dependent Claim
56 words
The method of claim 1 wherein the step of extracting a multi-frame feature includes setting the multi-frame feature to n, wherein n is a positive integer which indicates that the magnitude of each of the plurality of individual-frame features corresponding to n data frames precedingly adjacent to the specific data frame has persisted within a range.
Claim 7
Dependent Claim
299 words
In a system for recognizing a plurality of characters from a sample of handwritten text, the system utilizing a classifier which responds to a plurality of features extracted from the sample of handwritten text, a method for extracting the plurality of features, the method comprising the steps of: (a) receiving the sample of handwritten text; (b) sampling the handwritten text, over time, to form a sequences of sample datum, (c) partitioning the sequence of sample datum into a temporal sequence of data frames, each of the temporal sequence of data frames including at least two of the sequence of sample datum; (d) extracting a plurality of individual-frame feature from the temporal sequence of data frames, each of the plurality of individual-frame features having a magnitude and corresponding to one of the temporal sequence of data frames, wherein at least one of the individual-frame features includes a plurality of coefficients of a first order polynomial which is fitted to a angular velocity profile: v.sub.k =b.sub.0 +b.sub.1 v.sub.(k-1) +b.sub.2 v.sub.(k-2) +b.sub.3 v.sub.(k-3) wherein v.sub.(k) represents the angular velocity of a kth sample datum, v.sub.(k-1) represents the angular velocity of a (k-1)th sample datum, v.sub.(k-2) represents the angular velocity of a (k-2)th sample datum, v.sub.(k-3) represents the angular velocity of a (k-3)th sample datum, k is an integer index, and b 0 , b 1 , b 2 , and b 3 represent the coefficients of the first order polynomial; and (e) extracting a multi-frame feature, corresponding to a specific data frame of the temporal sequence of data frames, from one of: at least two of the plurality of individual-frame features, at least two of the temporal sequence of data frames, and at least one of the plurality of individual-frame features and at least one of the temporal sequence of data frames.
Claim 8
Dependent Claim
299 words
In a system for recognizing a plurality of characters from a sample of handwritten text, the system utilizing a classifier which responds to a plurality of features extracted from the sample of handwritten text, a method for extracting the plurality of features, the method comprising the steps of: (a) receiving the sample of handwritten text; (b) sampling the handwritten text, over time, to form a sequences of sample datum, (c) partitioning the sequence of sample datum into a temporal sequence of data frames, each of the temporal sequence of data frames including at least two of the sequence of sample datum; (d) extracting a plurality of individual-frame feature from the temporal sequence of data frames, each of the plurality of individual-frame features having a magnitude and corresponding to one of the temporal sequence of data frames, wherein at least one of the individual-frame features includes a plurality of coefficients of a first order polynomial which is fitted to a angular velocity profile: v.sub.k =b.sub.0 +b.sub.1 v.sub.(k-1) +b.sub.2 v.sub.(k-2) +b.sub.3 v.sub.(k-3) wherein v.sub.(k) represents the angular velocity of a kth sample datum, v.sub.(k-1) represents the angular velocity of a (k-1)th sample datum, v.sub.(k-2) represents the angular velocity of a (k-2)th sample datum, v.sub.(k-3) represents the angular velocity of a (k-3)th sample datum, k is an integer index, and b 0 , b 1 , b 2 , and b 3 represent the coefficients of the first order polynomial; and (e) extracting a multi-frame feature, corresponding to a specific data frame of the temporal sequence of data frames, from one of: at least two of the plurality of individual-frame features, at least two of the temporal sequence of data frames, and at least one of the plurality of individual-frame features and at least one of the temporal sequence of data frames.
Claim 9
Dependent Claim
34 words
The method of claim 6 wherein the plurality of individual-frame features includes a feature selected from the group consisting of angular velocity, curvilinear velocity, stroke angle, stroke curvature, data frame index, and Fourier coefficients.
Claim 10
Dependent Claim
30 words
The method of claim 6 wherein step (d) includes deriving the plurality of individual-frame features from coordinates of the sample of handwritten text, over time, as the sample is written.
Claim 11
Dependent Claim
30 words
The method of claim 6, wherein step (e) includes the sub-step of: generating the multi-frame feature based on a comparison of magnitudes of at least two of the individual-frame features.
Claim 12
Dependent Claim
54 words
The method of claim 6 wherein step (e) includes the sub-step of: setting the multi-frame feature to n, wherein n is a positive integer which indicates that the magnitude of each of the plurality of individual-frame features corresponding to n data frames precedingly adjacent to the specific data frame has persisted within a range.
Claim 13
Dependent Claim
175 words
In a system for recognizing handwritten text, a sub-system for extracting a plurality of features, the sub-system comprising: a frame extractor partitioning a sequence of sample datum derived from the handwritten text into a temporal sequence of data frames, each of the temporal sequence of data frames including at least two of the sequence of sample datum; and a feature extractor for extracting a plurality of individual-frame features and a multi-frame feature from the temporal sequence of data frames, wherein at least one of the individual-frame features includes a plurality of coefficients of a first order polynomial which is fitted to a velocity profile v.sub.k =b.sub.0 +b.sub.1 v.sub.(k-1) +b.sub.2 v.sub.(k-2) +b.sub.3 v.sub.(k-3) wherein v.sub.(k) represents the velocity of a kth sample datum, v.sub.(k-1) represents the velocity of a (k-1)th sample datum, v.sub.(k-2) represents the velocity of a (k-2)th sample datum, v.sub.(k-3) represents the velocity of a (k-3)th sample datum, k is an integer index, and b 0 , b 1 , b 2 , and b 3 represent the coefficients of the first order polynomial.
Claim 14
Dependent Claim
175 words
In a system for recognizing handwritten text, a sub-system for extracting a plurality of features, the sub-system comprising: a frame extractor partitioning a sequence of sample datum derived from the handwritten text into a temporal sequence of data frames, each of the temporal sequence of data frames including at least two of the sequence of sample datum; and a feature extractor for extracting a plurality of individual-frame features and a multi-frame feature from the temporal sequence of data frames, wherein at least one of the individual-frame features includes a plurality of coefficients of a first order polynomial which is fitted to a velocity profile v.sub.k =b.sub.0 +b.sub.1 v.sub.(k-1) +b.sub.2 v.sub.(k-2) +b.sub.3 v.sub.(k-3) wherein v.sub.(k) represents the velocity of a kth sample datum, v.sub.(k-1) represents the velocity of a (k-1)th sample datum, v.sub.(k-2) represents the velocity of a (k-2)th sample datum, v.sub.(k-3) represents the velocity of a (k-3)th sample datum, k is an integer index, and b 0 , b 1 , b 2 , and b 3 represent the coefficients of the first order polynomial.
Claim 15
Dependent Claim
14 words
The sub-system of claim 11 wherein the velocity profile is an angular velocity profile.
Claim 16
Dependent Claim
14 words
The sub-system of claim 11 wherein the velocity profile is an curvilinear velocity profile.
Claim 17
Dependent Claim
34 words
The sub-system of claim 11 wherein the plurality of individual-frame features includes a feature selected from the group consisting of angular velocity, curvilinear velocity, stroke angle, stroke curvature, data frame index, and Fourier coefficients.
Claim 18
Dependent Claim
24 words
The sub-system of claim 11 wherein the plurality of individual-frame features are extracted from the sample of handwritten text as the sample is written.
Claim 19
Dependent Claim
25 words
The sub-system of claim 11 wherein the feature extractor generates a multi-frame feature based on a comparison of at least two of the individual-frame features.
Claim 20
Dependent Claim
35 words
The sub-system of claim 11 wherein the feature extractor sets a multi-frame feature to n, wherein n is a positive integer which indicates that a sequence of n individual-frame features has persisted within a range.
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) | 2001-05-25 | 2001-11-24 | 2002-05-26 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-05-25 | 2005-11-24 | 2006-05-26 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-05-25 | 2009-11-24 | 2010-05-26 | $7700.00 | Window Closed / Lapsed |