FEATURE DESIGN FOR HMM-BASED HANDWRITING RECOGNITION

• US 20110280484A1
• Filed: 05/12/2010
• Published: 11/17/2011
• Est. Priority Date: 05/12/2010
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: MA, Lei, HUO, Qiang
• Application Number:
  US12/778,155
• Publication Number:
  US 20110280484A1
• Time in Patent Office: Days
• US Class Current: 382/185
• CPC Class Codes: G06V 30/287 of Kanji, Hiragana or Katakana characters
• CPC Class Codes: G06V 30/347 Sampling; Contour coding; Stroke extraction
• CPC Class Codes: G06V 30/36 Matching; Classification

Abstract

The disclosed architecture is a new feature extraction approach to handwriting recognition. Given an handwriting sample (e.g., from an online source), a sequence of time-ordered dominant points are extracted, which include stroke-endings, points corresponding to local extrema of curvature, and points with a large distance to the chords formed by pairs of previously identified neighboring dominant points. At each dominant point, a multi-dimensional feature vector is extracted, which includes a combination of coordinate features, delta features, and double-delta features.

Claims

1. A computer-implemented handwriting recognition system having computer readable media that store executable instructions executed by a processor, comprising:

a detection component that receives a handwriting sample, analyzes the handwriting sample for time-ordered dominant points, and outputs the dominant points; and

a feature extraction component that processes the dominant points and generates feature vectors for the dominant points, the feature vectors include coordinate features.

2. The system of claim 1, wherein the handwriting sample includes an Asian character.

3. The system of claim 1, wherein the dominant points include stroke endings.

4. The system of claim 1, wherein the dominant points include points associated with local extrema of curvature.

5. The system of claim 1, wherein the dominant points include points with a large distance to chords formed by pairs of previously identified neighboring dominant points.

6. The system of claim 1, wherein the feature vectors include at least one of coordinate features, delta features, or acceleration features.

7. The system of claim 1, wherein the feature vectors are multi-dimensional and further include at least one of delta features or acceleration features.

8. The system of claim 1, wherein each character class of the feature vectors is modeled by using a hidden Markov model (HMM).

9. A computer-implemented handwriting recognition method executed via a processor, comprising:

receiving an Asian handwriting sample of multiple strokes;

normalizing the sample;

converting the normalized sample of strokes into points and line segments;

analyzing the converted sample for dominant points; and

generating a sequence of feature vectors at the dominant points.

10. The method of claim 9, further comprising modeling each character class of the feature vectors using a continuous density HMM.

11. The method of claim 9, further comprising removing redundant points in the converted sample based on distance to a previous point.

12. The method of claim 9, further comprising removing a stroke based on distance between points and length of the stroke.

13. The method of claim 9, further comprising characterizing the dominant points as including at least one of stroke endings, points associated with local extrema of curvature, or points with a maximum distance to chords formed by pairs of previously identified neighboring dominant points.

14. The method of claim 9, further comprising characterizing the feature vectors as including coordinate features and at least one of delta features or acceleration features.

15. A computer-implemented handwriting recognition method executed via a processor, comprising:

receiving an East Asian handwriting sample of multiple strokes;

normalizing the sample using linear mapping that preserves an aspect ratio of the sample;

converting the normalized sample of strokes into points and line segments;

removing redundant points in the converted sample based on distance to a previous point;

removing a stroke based on distance between points and length of the stroke;

analyzing the converted sample for dominant points; and

generating a sequence of feature vectors at the dominant points each of which includes coordinate features.

16. The method of claim 15, further comprising characterizing the dominant points as including at least one of stroke endings or points where a trajectory direction changes more than a predetermined angle in degrees.

17. The method of claim 15, further comprising characterizing the dominant points as including points having a maximum distance to a chord formed by a pair of previously identified neighboring dominant points.

18. The method of claim 15, further comprising extracting a feature vector at each dominant point as a multi-dimensional vector.

19. The method of claim 15, further comprising characterizing the feature vectors as further including delta features.

20. The method of claim 15, further comprising characterizing the feature vectors as further including acceleration features.