1. Features for handwriting recognition

2. | 2 The challenge “Rappt JD 10 Feb no 175, om machtiging om af”

3. | 3 Short processing pipeline “machtiging” Feature extraction Classification 82,34,66,… 0.12 “machtiging” Learning

4. | 4 Processing pipeline Feature extraction Classification Preprocessing

5. | 5 Input image types ›Color: ›Grayscale: ›Binary:

6. Preprocessing ›Goal: enhance the foreground while reducing other visual symptoms (stains, noise, pictures,...) ›Methods: Contrast stretching Highpass filtering Despeckling Change color representation (RGB, HSV, grayscale, black/white, …) Remove selected connected components (  ) … | 6

7. | 7 Connected components

8. | 8 Processing pipeline Segmentation Feature extraction Classification Preprocessing

9. | 9 Object of classification ›Sentences ›Words ›Characters (use grammar) (use dictionary) (use alphabet)

10. | 10 Object representations ›Image ›Unordered vectors (in a coco) ›Contour vectors ›On-line vectors ›Skeleton image ›Skeleton vectors (x, y) i (x, y) k I(x, y)

11. | 11 A full processing pipeline Segmentation Normalization Feature extraction Classification Preprocessing

12. | 12 Invariance ›Luminance / contrast ›Position ›Size ›Rotation ›Shear ›Writer style ›Ink thickness ›…›…

13. | 13 Invariance by normalization ›Luminance / contrast ›Position ›Size ›Rotation ›Shear ›Writer style ›Ink thickness ›… Center on center of gravity Contrast stretching Scale to standard size

14. | 14 Invariance by trying many deformations ›Luminance / contrast ›Position ›Size ›Rotation ›Shear ›Writer style ›Ink thickness ›… Try different scale factors Try different rotations … and use the best recognition result Try different deformations

15. | 15 Invariance by using invariant features ›Luminance / contrast ›Position ›Size ›Rotation ›Shear ›Writer style ›Ink thickness ›… Zernike invariant moments

16. | 16 A full processing pipeline Segmentation Normalization Feature extraction Classification Preprocessing 82,34,66,…

17. | 17 Feature ROI types ›Whole object ›Zones ›Windowing

18. | 18 Whole object (“wholistic”)

19. | 19 Zones

20. | 20 Windowing

21. | 21 Feature types ›Image itself ›Statistical ›Structural ›Abstract ›Image (off-line) features (1—20) ›Contour / on-line features (21 – 28)

22. | 22 Feature 1 – 3 ›Connected component images ›Scaled image ›Distance transform (on whiteboard)

23. | 23 Feature 4: density histogram

24. | 24 Feature 5: radon transform

25. | 25 Feature 6: run count pattern 3 6 23

26. | 26 Feature 7: run length pattern avg stdev avg stdev

27. | 27 Feature 8: Autocorrelation

28. | 28 Feature 9: Polar zones

29. | 29 Feature 10: radial zones (tip!)

30. | 30 Feature 11: zone histograms

31. Feature 12: Hinge | 31 (By Marius Bulacu)

32. Feature 13: Fraglets | 32

33. | 33 Regelmatigheden Singulariteiten Feature 14: J.C. Simon (1/2)

34. | 34 "million" ==> convex:concave:3(north:concave) :(north:LOOP):concave:(north:LOOP) :concave:north :concave:HOLE :2(convex:concave) (J.-C. Simon, 1989) Feature 14: J.C. Simon (2/2)

35. | 35 Feature 15: Structure of background (1/3)

36. | 36 Feature 15: Structure of background (2/3)

37. | 37 Feature 15: Structure of background (3/3)

38. | 38 Feature 16: Structure of foreground + background

39. | 39 Feature 17: Fourier transform (1/2) From: http://ccp.uchicago.edu/~dcbradle/pages/5.23.06.html

40. | 40 Feature 17: Fourier transform (2/2) Fig. 1 and 3 from: http://www.csse.uwa.edu.au/~wongt/matlab.html Fig. 2 from: http://www.chemicool.com/definition/fourier_transform.html

41. | 41 Feature 18: Wavelet transform From: http://www.regonaudio.com/Audio%20Measurement%20via%20Wavelets.html

42. | 42 Feature 19: Hu invariant moments area of the object center of mass Slide from: http://www.cedar.buffalo.edu/~govind/CSE717/lectures/CSE717_3.ppt ›Invariant for scale, position and rotation ›Derived from moments ›Moments describe the image distribution with respect to its axes ›Works on (x, y) vectors

43. | 43 Feature 20: Zernike moments From: Trier, O. D., Jain, A. K., and Taxt, T. (1996). Feature extraction methods for character recognition - a survey. Pattern Recognition,29:641–662.

44. | 44 Feature 21 – 28: Contour features ›(cos, sin) of running angle ›(cos, sin) of running angular difference ›Angular difference ›Fourier transform ›Ink density (horizontal or vertical) ›Radon transform: (ink density, computed radially from the c.o.g.) ›Angular histogram ›Curvature scale space (  )

45. | 45 Feature 28: Curvature scale space From: http://www.christine.oppe.info/blog/category/formen-und-farben/formenvergleich/ pos iteration

46. End