HANDWRITING RECOGNITION METHOD AND DEVICE

• US 20120014601A1
• Filed: 06/23/2010
• Published: 01/19/2012
• Est. Priority Date: 06/24/2009
• Status: Abandoned Application

• Current Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Jtekt Corporation (Toyota Motor Corporation)
• Inventors: Wu, Bo, Wu, Yadong, Miao, Wei, Jiang, Shuhong, Li, Ailong
• Application Number:
  US13/258,084
• Publication Number:
  US 20120014601A1
• Time in Patent Office: Days
• US Class Current: 382/173
• CPC Class Codes: G06F 1/1626 with a single-body enclosure integrating a flat display, e.g. Personal Digital Assistants [PDAs]
• CPC Class Codes: G06F 1/1643 the display being associated to a digitizer, e.g. laptops that can be used as penpads details related to the relative motion of the display enclosure with respect to the body enclosure, e.g. to move between laptop and tablet PC configuration G06F1/1615
• CPC Class Codes: G06F 1/169 the I/O peripheral being an integrated pointing device, e.g. trackball in the palm rest area, mini-joystick integrated between keyboard keys, touch pads or touch stripes G06F1/1643 takes precedence; constructional details of pointing devices G06F3/033
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06V 30/347 Sampling; Contour coding; Stroke extraction
• CPC Class Codes: G06V 30/36 Matching; Classification

Abstract

A handwriting recognition method and a handwriting recognition device are provided to recognize a character sequence continuously inputted by a user for convenience. The present method comprises steps of calculating various features of the inputted character sequence which include single character recognition accuracy features and space geometry features of different stroke combinations in the inputted character sequence, calculating segmentation reliabilities of respective stroke combinations in different segmented patterns by using a probabilistic model in which coefficients of the probabilistic model are estimated by a parameter estimation method through sample trainings, recognizing characters in different writing patterns by using a multiple-template matching method when performing single character recognition of the stroke combinations, searching for the best segmentation path and conducting post-processing to optimize the recognition results. The present method and device have advantages of simple structure, low hardware requirement, fast recognition speed and high recognition accuracy and can be implemented in an embedded system.

Claims

1. A handwriting recognition method for recognizing a character sequence continuously inputted by a user, comprising:

calculating features relative to single character recognition accuracies of different stroke combinations in the inputted character sequence based on single character recognition results of different stroke combinations and sub-stroke combinations formed by segmenting strokes in the stroke combinations;

determining space geometry features of the different stroke combinations according to space geometry relationships of the sub-stroke combinations formed by segmenting strokes in the stroke combinations;

determining segmentation reliabilities of respective stroke combinations of the inputted character sequence in different segmented patterns based on the features relative to single character recognition accuracies and the space geometry features;

determining segmentation paths based on the segmentation reliabilities, and

presenting character sequence recognition results according to the determined segmentation paths to the user.

2. The method of claim 1, wherein a multiple-template matching method is adopted to recognize characters in different writing patterns for obtaining the single character recognition results.

3. The method of claim 1, further comprising:

performing post-processing of the character sequence recognition by using a dictionary database or a language model.

4. The method of claim 1, wherein the features relative to the accuracies of single character recognition comprise at least one of a single character recognition accuracy of a merged sub-stroke combination, a difference between the single character recognition accuracies of the merged sub-stroke combination and the sub-stroke combinations, and a ratio of the first candidate'"'"'s single character accuracy to the other candidate'"'"'s single character accuracy of the merged sub-stroke combination, and

the space geometry features of the stroke combinations comprise at least one of a gap between bounding boxes of the sub-stroke combinations, a width of the merged sub-stroke combination, a vector between the end point of the previous sub-stroke combination and the start point of the next sub-stroke combination, a distance between the end point of the previous sub-stroke combination and the start point of the next sub-stroke combination, and a distance between the start point of the previous sub-stroke combination and the start point of the next sub-stroke combination.

5. The method of claim 1, wherein determining the segmentation reliabilities comprises calculating segmentation reliabilities of respective stroke combinations of the inputted character sequence in different segmented patterns by using a Logistic Regression Model.

6. The method of claim 5, wherein the risk factors of the Logistic Regression Model are various kinds of features of stroke combinations.

7. The method of claim 5, wherein an intercept and regression coefficients of the Logistic Regression Model are estimated by sample trainings.

8. The method of claim 1, wherein determining segmentation reliabilities comprises calculating segmentation reliabilities of the inputted character sequence in different segmented patterns by a normal distribution model based on features of the inputted character sequence.

9. The method of claim 1, wherein determining segmentation paths based on the segmentation reliabilities comprises calculating the segmentation paths by using an N-best method or a dynamic programming method.

10. The method of claim 1, wherein presenting character sequence recognition results comprises presenting to the user the character sequence recognition results and at least a part of candidates of the character sequence recognition results.

11. The method of claim 10, wherein in response to a selection of candidate segmented patterns, the character sequence recognition results in the selected segmented pattern are presented to the user.

12. The method of claim 10, wherein in response to a selection of a single character, the character sequence recognition results including the selected single character are presented to the user.

13. A handwriting recognition device for recognizing a character sequence continuously inputted by a user, comprising:

a handwriting input unit configured to collect the character sequence continuously inputted by the user;

a single character recognition unit configured to obtain single character recognition results by recognizing different stroke combinations in the character sequence;

a segmentation unit configured to calculate features relative to single character recognition accuracies of different stroke combinations in the inputted character sequence based on the single character recognition results of the different stroke combinations and sub-stroke combinations formed by segmenting strokes in the stroke combinations, to determine space geometry features of the different stroke combinations according to space geometry relationships of the sub-stroke combinations, to determine segmentation reliabilities of respective stroke combinations of the inputted character sequence in different segmented patterns based on the features relative to single character recognition accuracies and the space geometry features, and to determine segmentation paths based on the segmentation reliabilities, and

a display control unit configured to control a display screen to present to the user the recognition results of the character sequence according to the determined segmentation paths.

14. The device of claim 13, wherein the single character recognition unit recognizes characters in different writing patterns by using a multiple-template matching method.

15. The device of claim 13, further comprising:

a post-processing unit configured to perform the post-processing of the character sequence recognition by using a dictionary database or a language model.

16. The device of claim 13, wherein the features relative to the accuracies of single character recognition comprise at least one of a single character recognition accuracy of a merged sub-stroke combination, a difference between the single character recognition accuracies of the merged sub-stroke combination and the sub-stroke combinations, and a ratio of the first candidate'"'"'s single character accuracy to the other candidate'"'"'s single character accuracy of the merged sub-stroke combination, and

the space geometry features of the stroke combinations comprise at least one of a gap between bounding boxes of the sub-stroke combinations, a width of the merged sub-stroke combination, a vector between the end point of the previous sub-stroke combination and the start point of the next sub-stroke combination, a distance between the end point of the previous sub-stroke combination and the start point of the next sub-stroke combination, and a distance between the start point of the previous sub-stroke combination and the start point of the next sub-stroke combination.

17. The device of claim 13, wherein the segmentation unit calculates segmentation reliabilities of respective stroke combinations of the inputted character sequence in different segmented patterns by using a Logistic Regression Model.

18. The device of claim 13, wherein the segmentation unit calculates segmentation reliabilities of the inputted character sequence in different segmented patterns by a normal distribution model based on features of the inputted character sequence.

19. The device of claim 13, wherein the segmentation unit calculates the segmentation paths by using an N-best method or a dynamic programming method.

20. The device of claim 13, wherein the display control unit further controls the display screen to present to the user the character sequence recognition results and at least a part of candidates of the character sequence recognition results.

21. The device of claim 20, wherein in response to a selection of candidate segmented patterns, the display control unit controls the display screen to present the character sequence recognition results in the selected segmented pattern to the user.

22. The device of claim 20, wherein in response to a selection of a single character, the display control unit controls the display screen to present the character sequence recognition results including the selected single character to the user.

23. The device of claim 17, wherein risk factors of the Logistic Regression Model are various features of stroke combination.

24. The device of claim 17, wherein an intercept and regression coefficients of the Logistic Regression Model are estimated by sample trainings.