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

• US 10,496,872 B2
• Filed: 07/20/2018
• Issued: 12/03/2019
• 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:
  US16/041,506
• Publication Number:
  US 20180330148A1
• Time in Patent Office: 501 Days
• Field of Search: 382123
• 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 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 encoding 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.

2. The method of claim 1, wherein the entropy coding method comprises switching between variable length code tables according to a stream type.

3. The method of claim 1, wherein the handwriting data values include at least data for an X coordinate and a Y coordinate.

4. The method of claim 1, further comprising,

encoding a 1st value and a 2nd value in the sequence of handwriting data values by a different encoding scheme without prediction residual values;

wherein the kth value is 3^rd or later in the sequence of handwriting data values.

5. The method of claim 1 further comprising encoding at least some handwriting data using a delta encoding scheme.

6. A method executed in a computer to reproduce a compressed handwriting data generated by a handwriting data input device, the method comprising:

decoding at least (k−

1)th and (k−

2)th values of previous handwriting data;

decoding a kth prediction residual value from the compressed handwriting data by decoding a prediction residual value coded by an entropy coding method that maps shorter bitstrings for smaller absolute prediction residual values and longer bitstrings for larger absolute prediction residual values; and

restoring a kth original value based on (a) a prediction value derived from the at least (k−

1)th and (k−

2)th values of previous handwriting data, and (b) the decoded prediction residual value.