Preprocessing of multi-line rotated electronic ink

• US 20030210817A1
• Filed: 05/10/2002
• Published: 11/13/2003
• Est. Priority Date: 05/10/2002
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Abdulkader, Ahmad, Hullender, Gregory
• Granted Patent: US 7,010,165 B2
• Time in Patent Office: Days
• US Class Current: 382/187
• CPC Class Codes: G06V 30/10 Character recognition
• 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 30/1478 of characters or characters lines

Abstract

An electronic ink object is preprocessed for improving handwriting recognition results. An electronic ink object, possibly having multiple lines of non-horizontal ink, is preprocessed by segmenting or separating the electronic ink object into one or more lines, computing a writing angle of each line, and rotating each line so that it is horizontal.

Claims

1. A method of preprocessing an electronic ink object comprising the steps of:

(a) segmenting the electronic ink object into at least one line;

(b) estimating a writing angle of each line; and

(c) rotating each line to make horizontal a centerline of the line.

2. The method of preprocessing an electronic ink object of claim 1 further comprising the step of:

(d) recognizing the segmented and rotated electronic ink object to identify at least one character.

3. The method of preprocessing an electronic ink object of claim 1 wherein the electronic ink object comprises at least one ink stroke and the step of segmenting includes the step of (i) identifying a plurality of features for each ink stroke of the electronic ink object;

and (ii) classifying each ink stroke as part of a new line, a current line, or a previous line.

4. The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a neural network.

5. The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a hidden Markov model.

6. The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a decision tree.

7. The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a Bayesian net.

8. The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a K Nearest Neighbor (KNN).

9. The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using an Expert System.

10. The method of preprocessing an electronic ink object of claim 3 wherein if the ink stroke is part of the new line, the step of estimating includes the step of computing a slope of the new line.

11. The method of preprocessing an electronic ink object of claim 3 wherein if the ink stroke is part of the new line, the step of estimating includes the step of computing a slope of a centerline of the new line using a Least Mean Squares algorithm.

12. The method of preprocessing an electronic ink object of claim 3 wherein if the ink stroke is part of the current line, the step of estimating includes the step of re-computing a slope of the current line.

13. The method of preprocessing an electronic ink object of claim 3 wherein if the ink stroke is part of the current line, the step of estimating includes the step of re-computing a slope of a centerline of the current line using a Least Mean Squares algorithm.

14. The method of preprocessing an electronic ink object of claim 3 wherein if the ink stroke is part of the previous line, the step of estimating includes the step of re-computing a slope of the previous line.

15. The method of preprocessing an electronic ink object of claim 3 wherein if the ink stroke is part of the previous line, the step of estimating includes the step of re-computing a slope of a centerline of the previous line using a Least Mean Squares algorithm.

16. The method of preprocessing an electronic ink object of claim 1 wherein the step of rotating is performed using a rotation matrix.

17. A computer-readable medium having computer-executable instructions for performing the steps recited in claim 1.

18. A system for processing an electronic ink object having at least one ink stroke comprising in combination:

(a) an input that receives the electronic ink object;

(b) an electronic ink feature generator that receives the inputted electronic ink object and generating at least one feature for the at least one ink stroke of the electronic ink object;

(c) a classifier that classifies the at least one ink stroke of the electronic ink object into at least one line based on the feature of the ink stroke;

(d) a centerline generator that generates a centerline of the at least one line of the electronic ink object; and

(e) an ink transformer that rotates the at least one line of electronic ink based on the centerline.

19. The system of claim 18 wherein the classifier is a neural network.

20. The system of claim 18 wherein the classifier is a hidden Markov model.

21. The system of claim 18 wherein the classifier is a decision tree.

22. The system of claim 18 wherein the classifier is a Bayesian net.

23. The system of claim 18 wherein the classifier is a K Nearest Neighbor (KNN).

24. The system of claim 18 wherein the classifier is an Expert System.

25. The system of claim 18 further comprising:

(f) a character recognizer that receives the segmented and rotated electronic ink object to identify at least one character.

26. The system of claim 18 further comprising:

(e) a user interface that generates the electronic ink object.

27. The system of claim 26 wherein the user interface is a display surface.

28. A system for processing an electronic ink object having at least one ink stroke comprising in combination:

(a) an input that receives the electronic ink object;

(b) a centerline generator that generates a centerline of the electronic ink object; and

(c) an ink transformer that rotates the electronic ink object based on the centerline.

29. The system of claim 28 further comprising:

(d) a character recognizer that receives the rotated electronic ink object to identify at least one character.