features for handwriting recognition

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

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