System and method of gesture feature recognition

• US 20070263932A1
• Filed: 05/12/2006
• Published: 11/15/2007
• Est. Priority Date: 05/12/2006
• Status: Abandoned Application

• Current Assignee: Waterloo Maple Incorporated (Fuji Soft Incorporated)
• Original Assignee: Waterloo Maple Incorporated (Fuji Soft Incorporated)
• Inventors: Bernardin, Laurent, Wang, Yu-Hong
• Application Number:
  US11/433,234
• Publication Number:
  US 20070263932A1
• Time in Patent Office: Days
• US Class Current: 382/187
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06V 30/36 Matching; Classification

Abstract

A gesture feature recognition system and method is provided. The gesture feature recognition system comprises an input/output component for recording pen strokes and for displaying them as strokes on a display device, a repository for storing gesture recognition data and a recognizer component for analyzing the strokes using the gesture recognition data. The method comprises the steps of receiving input data that represents the stroke sequence of handwritten characters and symbols, comparing the input data to a predefined symbol set stored in a repository, identifying characters and symbols present in the input data, and identifying the location of relevant features of the characters and symbols.

Claims

1. A gesture feature recognition system, the gesture feature recognition system comprising:

an input/output component for recording pen strokes and for displaying them as strokes on a display device;

a repository for storing gesture recognition data; and

a recognizer component for analyzing the strokes using the gesture recognition data.

2. The gesture feature recognition system as claimed in claim 1, wherein the input/output component includes a display panel that allows a user to input pen strokes using a pointing device.

3. A method of gesture feature recognition, the method comprising the steps of:

receiving input data that represents the stroke sequence of handwritten characters and symbols;

comparing the input data to a predefined symbol set stored in a repository;

identifying characters and symbols present in the input data; and

identifying the location of relevant features of the characters and symbols.

4. The method as claimed in claim 3, further comprising the step of displaying digital characters or symbols that correspond to the identified characters or symbols.

5. The method as claimed in claim 3, further comprising the step of outlining the identified relevant features in the display.

6. A method for encoding strokes representing characters or symbols using shape primitives with interspersed feature location information.

7. A method for encoding strokes representing characters and symbols using primitive shapes like lines, arcs, points and loops, with interspersed information for locating features like baselines and particular points and regions in hand-drawn symbols.

8. A method for creating a repository of sequences of shape primitives and feature location information, that can be used by a symbol recognition.

9. A method for matching handwritten characters against a model structure that contains feature location information.

10. A method for mapping feature location information found within a model symbol to the corresponding location within a handwritten symbol.

11. A method for detection the baseline of handwritten characters with high precision.

12. A method for differentiating between the inside and outside region of a hand-drawn root symbol.