1. Loop Investigation for Cursive Handwriting Processing and Recognition By Tal Steinherz Advanced Seminar (Spring 05)

2. Outline Background on cursive handwriting Introduction to loops Pattern recognition and machine learning conflicts Feature extraction solutions Demonstrations and experimental results

3. Cursive Handwriting (J. C. Simon) “ Displacing a pen from left to right in an oscillating movement, with loops, descendants (legs), and ascendants (poles). ”

4. Cursive vs. Character Cursive – continuous concatenated set of strokes. produced by a human being in a free style. Character – a single standalone symbol. produced by a machine subjected to numerous alternative fonts.

5. Online vs. Offline Online – captured by pen-like devices. the input format is a two-dimensional signal of pixel locations as a function of time (x(t),y(t)). Offline – captured by scanning devices. the input format is a two-dimensional image of gray- scale colors as a function of location I(m * n). strokes have significant width.

6. Online vs. Offline (demo)

7. A Loop (T. Steinherz) “ A set of neighboring foreground pixels surrounding a hole, i.e., a connected blocked group of background pixels in the word ’ s image, where all foreground pixels are within stroke width distance from the hole. ”

8. Ascending (Descending) Loops

9. Axial (of the middle zone) Loops

10. The importance of loops Shared by many letters (especially a,d,e,g,o,p,q) Byproduct of the continuous nature of cursive handwriting (like with b,f,h,j,k,l,s,t,y,z) Elementary and prominent features Carry additional information given by a set of descriptive parameters

11. The motivation to investigate loops Character recognition supports discrimination between letters. Writer modeling Identification Examination contributes to applications in forensic science and graphology.

12. The output of loop investigation Incomplete (open) loop identification Hidden (collapsed) loop tracking - locating blobs that correspond to online loops Multi (encapsulated) loops understanding - distinguishing natural from artificial loops Temporal information recovery - retracing the original path of a pen

13. The Engineering Approach (J. C. Simon & T. Pavlidis) “ Requires understanding the structure of the objects to be recognized and apply the appropriate combination of (pattern recognition) techniques. ”

14. Feature extraction dilemmas Offline cursive word signal representation Loop identification Signal to noise ratio Feature vector translation The difficulties consist in the feature extraction and preprocessing rather than the machine learning \ recognition engine phase.

15. Offline cursive word signal representation We use the external upper and lower contours in conjunction with the internal contour of all visible loops.

16. Loop identification Given a set of singular points, identification is provided by correlation between pieces of the same contour (around anchor points), of the opposite contours and\or in association with subsets of internal contours.

17. Signal to noise ratio In order to improve the signal ’ s parametric quantifiability and reduce noisy artifacts, the contour is transformed to a polygon.

18. Hidden loop tracking - the mutual distance principle

19. Hidden loop tracking - the mutual distance principle (cont.)

21. Multi loops understanding - the continuity principle

22. Temporal information recovery -the matching principle

23. Hidden loop tracking - an application to ascending (descending) loops Writer#1Writer#2Writer#3Writer#4Writer#5Writer#6Total Number of words Number of characters Number of Loops (all kinds) 223219223170215223 11301113113083510831130 1273 6421 103912721013745133211466547

24. Hidden loop tracking - an application to ascending (descending) loops (cont.) Offline Loops Encapsulate d DisqualifiedFoundTotal Online Loops Real Loops Number Rate 259 100% 1006186519964 25.7%18.5%51.6%95.8%

25. Hidden loop tracking - an application to ascending (descending) loops (cont.) Offline Loops Encapsulate d DisqualifiedFoundTotal Online Loops Large Loops (8<) Number Rate 233 100% 856147341721 27.2%17.2%39.8%84.2% Offline Loops Encapsulate d DisqualifiedFoundTotal Online Loops Large Loops (6<) Number Rate 288 100% 1105177390855 26.1%16.0%35.3%77.4%

26. Hidden loop tracking - an application to ascending (descending) loops (cont.) Threshol d Small Loops No Loops Total 8180209389 6131 209340

27. Multi loops understanding - a classifier of beginning a-s More than 40 writers with 1-4 samples per writer.

28. Multi loops understanding - a classifier of beginning a-s Type AType BError Questionabl e Total Loops Number Rate 32/36 100% 81/9326/2816/217/8 39%/38%30%/32%19%/22%7.5%/8%