Features for handwriting recognition
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The challenge
“Rappt JD 10 Feb no 175, om machtiging om af”
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Short processing pipeline
“machtiging”
Feature extraction
Classification
82,34,66,…0.12
“machtiging”
Learning
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Processing pipeline
Feature extraction
Classification
Preprocessing
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Input image types
› Color:
› Grayscale:
› Binary:
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 ()• …
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Connected components
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Processing pipeline
Segmentation
Feature extraction
Classification
Preprocessing
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Object of classification› Sentences› Words› Characters
(use grammar)(use dictionary)(use alphabet)
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Object representations
› Image› Unordered vectors (in a coco)› Contour vectors› On-line vectors› Skeleton image› Skeleton vectors
(x, y)i
(x, y)k
(x, y)k
(x, y)k
I(x, y)
I(x, y)
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A full processing pipeline
Segmentation
Normalization
Feature extraction
Classification
Preprocessing
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Invariance
› Luminance / contrast› Position› Size› Rotation› Shear› Writer style› Ink thickness› …
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Invariance by normalization
› Luminance / contrast› Position› Size› Rotation› Shear› Writer style› Ink thickness› …
Center on center of gravity
Contrast stretching
Scale to standard
size
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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 deformation
s
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Invariance by using invariant features
› Luminance / contrast› Position› Size› Rotation› Shear› Writer style› Ink thickness› …
Zernike invariant moments
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A full processing pipeline
Segmentation
Normalization
Feature extraction
Classification
Preprocessing
82,34,66,…
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Feature ROI types
› Whole object› Zones› Windowing
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Whole object (“wholistic”)
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Zones
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Windowing
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Feature types
› Image itself› Statistical› Structural› Abstract
› Image (off-line) features (1—20)› Contour / on-line features (21 – 28)
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Feature 1 – 3
› Connected component images
› Scaled image
› Distance transform (on whiteboard)
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Feature 4: density histogram
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Feature 5: radon transform
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Feature 6: run count pattern
3
6
2 3
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Feature 7: run length pattern
avg
stdev
avg
stdev
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Feature 8: Autocorrelation
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Feature 9: Polar zones
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Feature 10: radial zones (tip!)
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Feature 11: zone histograms
Feature 12: Hinge
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(By Marius Bulacu)
Feature 13: Fraglets
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Regelmatigheden
Singulariteiten
Feature 14: J.C. Simon (1/2)
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"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)
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Feature 15: Structure of background (1/3)
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Feature 15: Structure of background (2/3)
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Feature 15: Structure of background (3/3)
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Feature 16: Structure of foreground + background
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Feature 17: Fourier transform (1/2)
From: http://ccp.uchicago.edu/~dcbradle/pages/5.23.06.html
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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
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Feature 18: Wavelet transform
From: http://www.regonaudio.com/Audio%20Measurement%20via%20Wavelets.html
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Feature 19: Hu invariant moments
dxdyyxyxiM q
D
pqp ).,(,
0,0M area of the object
0,11,0 ,MM 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
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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.
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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 ()
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Feature 28: Curvature scale space
From: http://www.christine.oppe.info/blog/category/formen-und-farben/formenvergleich/
pos
itera
tion
End
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