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Features for handwriting recognition

Features for handwriting recognition. The challenge. “Rappt JD 10 Feb no 175, om machtiging om af”. Short processing pipeline. Learning. “machtiging”. Feature extraction. Classification. 82,34,66,…. “machtiging”. 0.12. Processing pipeline. Preprocessing. Feature extraction.

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Features for handwriting recognition

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  1. Features for handwriting recognition

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

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

  4. Processing pipeline Preprocessing Feature extraction Classification

  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 () • …

  7. Connected components

  8. Processing pipeline Preprocessing Segmentation Feature extraction Classification

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

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

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

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

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

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

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

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

  17. Feature ROI types • Whole object • Zones • Windowing

  18. Whole object (“wholistic”)

  19. Zones

  20. Windowing

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

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

  23. Feature 4: density histogram

  24. Feature 5: radon transform

  25. 2 3 Feature 6: run count pattern 3 6

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

  27. Feature 8: Autocorrelation

  28. Feature 9: Polar zones

  29. Feature 10: radial zones (tip!)

  30. Feature 11: zone histograms

  31. Feature 12: Hinge (By Marius Bulacu)

  32. Feature 13: Fraglets

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

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

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

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

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

  38. Feature 16: Structure of foreground + background

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

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

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

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

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

  46. End

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