Features for handwriting recognition

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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
Learning
machtiging
Feature extraction
Classification
82,34,66,
machtiging
0.12
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Processing pipeline
Preprocessing
Feature extraction
Classification
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Input image types

• Color
• Grayscale
• Binary

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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
Preprocessing
Segmentation
Feature extraction
Classification
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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

I(x, y)
(x, y)i
(x, y)k
(x, y)k
I(x, y)
(x, y)k
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A full processing pipeline
Preprocessing
Segmentation
Normalization
Feature extraction
Classification
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Invariance

• Luminance / contrast
• Position
• Size
• Rotation
• Shear
• Writer style
• Ink thickness

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Invariance by normalization
Contrast stretching

• Luminance / contrast
• Position
• Size
• Rotation
• Shear
• Writer style
• Ink thickness

Center on center of gravity
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
Try different deformations
and use the best recognition result
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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
Preprocessing
Segmentation
Normalization
Feature extraction
Classification
82,34,66,
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Feature ROI types

• Whole object
• Zones
• Windowing

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Whole object (holistic)
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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 (120)
• Contour / on-line features (21 28)

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Feature 1 3

• Connected component images
• Scaled image
• Distance transform

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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
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Feature 7 run length pattern
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
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Feature 12 Hinge
(By Marius Bulacu)
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Feature 13 Fraglets
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Feature 14 J.C. Simon (1/2)
Singulariteiten
Regelmatigheden
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Feature 14 J.C. Simon (2/2)
"million" gt convexconcave3(northconcave
) (northLOOP)concave(northLOOP)
concavenorth concaveHOLE
2(convexconcave)
(J.-C. Simon, 1989)
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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/won
gt/matlab.html
Fig. 2 from http//www.chemicool.com/definition/f
ourier_transform.html
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Feature 18 Wavelet transform
From http//www.regonaudio.com/Audio20Measuremen
t20via20Wavelets.html
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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 adapted from http//www.cedar.buffalo.edu/
govind/CSE717/lectures/CSE717_3.ppt
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Feature 20 Zernike moments

• Invariant for scale, position and rotation
• Reconstructing original

• From Trier, O. D., Jain, A. K., and Taxt, T.
  (1996). Feature extraction methods for character
  recognition - a survey. Pattern
  Recognition,29641662.

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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
iteration
pos
From http//www.christine.oppe.info/blog/category
/formen-und-farben/formenvergleich/
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End