Contour-Based Digit Recognition
Chris Douglas
Motivation
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Normalized cross-correlation is fine for a single
font with a single size
Contours contain a lot of the information used
by humans to distinguish between characters
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Using only contours shrinks the problem size
Shape Classification Methods
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Descriptors
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Classification
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Fourier Descriptors
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K-means
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Zernike Moments
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K-nearest neighbors
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Legendre Moments
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Decision trees
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Statistical Moments
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Mahalanobis distance
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Hu's Invariant
Moments
Splines
Other descriptors
such as area,
eccentricity, height,
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Support Vector
Machines (SVM)
Neural networks
Process
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Assume training images are pre-classified
correctly, binary, and mostly free of
noise/occlusion
Take the contours of the image
Find the contour lists of the contours
('bwtraceboundary')
Mean center these lists and scale to a standard
size while preserving the overall shape of the
character
Rotate by angle from 'regionprops' 'orientation'
property
Process
(continued)
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Resize so each contour is the same amount of
points in length
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Isn't necessary for some descriptors
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Can be done with interpolation in the spatial domain
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Decided to carefully use the frequency domain and
pad an array of DFT coefficients with 0s to the
correct size
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Special considerations need to be made
Data
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Fonts and handwritten numbers with varying
degree of carelessness (dataset 1)
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1280 training images
Test data are handwritten samples by 2 willing
participants and paint-created samples
MNIST data base (dataset 2)
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60,000 training images
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10,000 test images
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The images seen thus far
Carefully handwritten numbers by one person
(dataset 3)
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1251 Training images
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210 Test images
Dataset 1 Results
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Single contour
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(K=5) All but a 2 & a 3 were classified correctly
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(K=4) All images were classified correctly
Multiple contour
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(K=5)
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Fourier misclassified a 3 as a 7
Absolute Difference of contour points misclassified the
same 3 as a 7, a 2 as a 1, and a 0 as a 1.
(K=1)
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Fourier misclassified a 7 as a 4, a 3 as a 7
Absolute difference misclassified the same 7 as a 4, and
a 0 as a 4
Dataset 2 Results
(single contour)
Dataset 2 Results
(multiple contour)
Dataset 3 Results
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Single contour
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(K=5) A 5 was misclassified as a 9
Multiple contour
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(K=5) Three 7s were misclassified as a 2,3,and a 5,
a 0 was misclassified as an 8
(K=1) A 7 was misclassified as a 2