Dynamic handwriting verification, handwriting-based user authentication, handwriting data generation, and handwriting data preservation

• US 10,032,065 B2
• Filed: 10/22/2014
• Issued: 07/24/2018
• Est. Priority Date: 10/25/2013
• Status: Active Grant

• Current Assignee: Wacom Co Limited (Wacom)
• Original Assignee: Wacom Co Limited (Wacom)
• Inventors: Holden, Martin, Mettyear, Nicholas
• Primary Examiner(s): Bayat, Ali
• Application Number:
  US15/031,700
• Publication Number:
  US 20160283777A1
• Time in Patent Office: 1,371 Days
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06V 10/754 involving a deformation of the sample pattern or of the reference pattern; Elastic matching
• CPC Class Codes: G06V 30/1423 the instrument generating sequences of position coordinates corresponding to handwriting preprocessing or recognising digital ink G06V30/32
• CPC Class Codes: G06V 30/2268 using stroke segmentation
• CPC Class Codes: G06V 40/30 Writer recognition; Reading and verifying signatures
• CPC Class Codes: G06V 40/33 based only on signature image, e.g. static signature recognition
• CPC Class Codes: G06V 40/37 based only on signature signals such as velocity or pressure, e.g. dynamic signature recognition
• CPC Class Codes: G06V 40/394 Matching; Classification

Abstract

Handwriting verification methods and related computer systems, and handwriting-based user authentication methods and related computer systems are disclosed. A handwriting verification method comprises obtaining a handwriting test sample containing a plurality of available parameters, extracting geometric parameters, deriving geometric features comprising an x-position value and a y-position value for each of a plurality of feature points in the test sample, performing feature matching between geometric features of the test sample and a reference sample, determining a handwriting verification result based at least in part on the feature matching, and outputting the handwriting verification result. Techniques and tools for generating and preserving electronic handwriting data also are disclosed. Raw handwriting data is converted to a streamed format that preserves the original content of the raw handwriting data. Techniques and tools for inserting electronic handwriting data into a digital image also are disclosed.

Claims

1. A method executed in a computer configured to be used with a handwriting data input device, the method comprising:

obtaining raw handwriting data comprising a sequence of pen events each including a plurality of values of different attributes;

generating, from the raw handwriting data, streamed data including a plurality of streams, each of which includes data related to a single attribute of the different attributes, wherein generating the streamed data comprises selecting a compression technique from a plurality of available compression techniques for each of the plurality of streams, and applying the selected compression technique to each of the plurality of streams;

generating, from the raw handwriting data, stroke data and/or image data; and

selectively outputting (i) the streamed data or (ii) the stroke data and/or image data.

2. The method of claim 1, wherein generating the streamed data comprises inserting identifiers associated with the plurality of streams, and wherein inserting the identifiers comprises inserting the identifiers in a header of the streamed data.

3. The method of claim 1, wherein the plurality of available compression techniques comprises delta coding, wherein the delta coding comprises run-length coding of delta values, and wherein applying the selected compression technique comprises applying the delta coding to x-coordinate values or y-coordinate values.

4. The method of claim 1, wherein the selected compression technique comprises:

obtaining a kth value in a sequence of handwriting data values as a current target value, where k is an index value;

calculating a predicted value for the kth value based on values obtained in advance of the encoding of the kth value;

calculating a prediction residual value for the kth value based on the current target value and the predicted value for the kth value; and

encoding the prediction residual value by using an entropy coding method that maps shorter bitstrings for smaller absolute prediction residual values and longer bitstrings for larger absolute prediction residual values.

5. The method of claim 4, wherein the entropy coding method comprises switching between variable length code tables according to a stream type.

6. The method of claim 1, wherein generating the streamed data comprises lossless data processing, and wherein generating the stroke data and/or the image data comprises lossy data processing.

7. The method of claim 6, wherein the lossless data processing comprises preserving timestamp information, and wherein the lossy data processing comprises discarding the timestamp information.

8. The method of claim 6, wherein the lossless data processing comprises preserving the plurality of values at an original level of precision, and wherein the lossy data processing comprises reducing the original level of precision for at least some of the plurality of values.

9. A method executed in a computer configured to be used with a handwriting data input device, the method comprising:

obtaining raw handwriting data comprising a sequence of pen events each including a plurality of values of different attributes;

generating, from the raw handwriting data, streamed data including a plurality of streams, each of which includes data related to a single attribute of the different attributes;

generating, from the raw handwriting data, stroke data and/or image data; and

selectively outputting (i) the streamed data or (ii) the stroke data and/or image data, wherein the selective outputting comprises integrating the streamed data with the stroke data and/or the image data, thereby recombining original content of the raw handwriting data with the stroke data and/or the image data in a single file.

10. The method of claim 9, wherein the integrating comprises a steganographic process.

11. The method of claim 9, wherein generating the streamed data comprises lossless data processing, and wherein generating the stroke data and/or the image data comprises lossy data processing.

12. The method of claim 11, wherein the lossless data processing comprises preserving timestamp information, and wherein the lossy data processing comprises discarding the timestamp information.

13. The method of claim 11, wherein the lossless data processing comprises preserving the plurality of values at an original level of precision, and wherein the lossy data processing comprises reducing the original level of precision for at least some of the plurality of values.

14. The method of claim 9, wherein generating the streamed data comprises inserting identifiers associated with the plurality of streams, and wherein inserting the identifiers comprises inserting the identifiers in a header of the streamed data.

15. A computing device configured to be used with a handwriting data input device, the computing device comprising non-transitory computer-readable media and one or more processors, the computing device being programmed to perform steps comprising:

obtaining raw handwriting data comprising a sequence of pen events each including a plurality of values of different attributes;

generating, from the raw handwriting data, streamed data including a plurality of streams, each of which includes data related to a single attribute of the different attributes;

generating, from the raw handwriting data, stroke data and/or image data;

selectively outputting (i) the streamed data or (ii) the stroke data and/or image data, wherein the selective outputting is based at least in part on a request from an application; and

rendering the outputted data for display.

16. The computing device of claim 15, wherein generating the streamed data comprises lossless data processing, and wherein generating the stroke data and/or the image data comprises lossy data processing.

17. A computing device comprising non-transitory computer-readable media and one or more processors, the computing device being programmed to perform steps comprising:

obtaining raw handwriting data comprising a sequence of pen events each including a plurality of values of different attributes;

generating, from the raw handwriting data, streamed data including a plurality of streams, each of which includes data related to a single attribute of the different attributes, wherein generating the streamed data comprises selecting a compression technique from a plurality of available compression techniques for each of the plurality of streams, and applying the selected compression technique to each of the plurality of streams;

generating, from the raw handwriting data, stroke data and/or image data; and

selectively outputting (i) the streamed data or (ii) the stroke data and/or image data.

18. The computing device of claim 17, wherein generating the streamed data comprises inserting identifiers associated with the plurality of streams, and wherein inserting the identifiers comprises inserting the identifiers in a header of the streamed data, and wherein the selecting of the compression techniques is based on the identifiers.

19. The computing device of claim 17, wherein the plurality of available compression techniques comprises delta coding, wherein the delta coding comprises run-length coding of delta values, and wherein applying the selected compression technique comprises applying the delta coding to x-coordinate values or y-coordinate values.

20. The computing device of claim 17, wherein the selected compression technique comprises:

obtaining a kth value in a sequence of handwriting data values as a current target value, where k is an index value;

calculating a predicted value for the kth value based on values obtained in advance of the encoding of the kth value;

calculating a prediction residual value for the kth value based on the current target value and the predicted value for the kth value; and

encoding the prediction residual value by using an entropy coding method that maps shorter bitstrings for smaller absolute prediction residual values and longer bitstrings for larger absolute prediction residual values.

21. The computer-readable media of claim 17, wherein generating the streamed data comprises lossless data processing, and wherein generating the stroke data and/or the image data comprises lossy data processing.

22. A computing device comprising non-transitory computer-readable media and one or more processors, the computing device being programmed to perform steps comprising:

obtaining raw handwriting data comprising a sequence of pen events each including a plurality of values of different attributes;

generating, from the raw handwriting data, streamed data including a plurality of streams, each of which includes data related to a single attribute of the different attributes;

generating, from the raw handwriting data, stroke data and/or image data; and

selectively outputting (i) the streamed data or (ii) the stroke data and/or image data, wherein the selective outputting comprises integrating the streamed data with the stroke data and/or the image data, thereby recombining original content of the raw handwriting data with the stroke data and/or the image data in a single file.

23. The computing device of claim 22, wherein generating the streamed data comprises lossless data processing, and wherein generating the stroke data and/or the image data comprises lossy data processing.

24. The computing device of claim 22, wherein generating the streamed data comprises inserting identifiers associated with the plurality of streams, and wherein inserting the identifiers comprises inserting the identifiers in a header of the streamed data.

25. The computing device of claim 22, wherein the integrating comprises a steganographic process.