Multiple Approaches at Hand Written Digit Recognition

1
Multiple Approaches at Hand Written Digit
Recognition

• Luis Bathen
• Mike Munson
• Jeremy Smith

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Problem and Motivation

• Problem
• Given picture data representing a handwritten
  digit, determine which digit (0-9) it is
• Error rate must be extremely low for practical
  use
• If the digit is too poorly drawn or cannot be
  classified, it should be rejected rather than
  risk improper classification
• Motivation
• Primary application Postal Mail (Automatic
  sorting of mail by destination ZIP code)
• Other applications Digitizing handwritten
  spreadsheets, tax forms, etc.

3
Alvarez, Roderiguez, Hermidia
Kirsch Gradients
Network Structure
4
Scaling the Image

• The algorithm adds the values of 4 pixels to get
  the new pixel's value.
• Resolution is halved, depth is doubled.
• This makes less nodes to train, and gives
  invariance to subtle differences.

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Edge Detection

• Output only certain edges from an image
  (vertical, horizontal, left-diagonal,
  right-diagonal) to new images.
• Using the Kirsch algorithm
• Goal is to create separate networks, trained over
  specific features of the image (one network for
  each edge map)

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The Image

• To extract locality from the image we used the
  Kirsch detector algorithm.
• G(i, j) max1, max5Sk- 3tk
• Where G(i, j) is the gradient for pixel (i, j)
  and K0..7
• SkAk Ak1 Ak2
• Tk Ak Ak1 Ak2 Ak3 Ak4

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Approaches

• Five subnet-multi-layered network (10 Class
  output)
• Raw 32x32/16x16/8x8 Binary Images (10 class
  output)
• Raw 3-bit value images 4x4 (10 Class output)
• 4x4 Binary/3-bit (Binary output)
• Simple image correlation (For Comparison)

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Five subnet-multi-layered network (10 Class
output)
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32x32/16x16/8x8 and10 class output
Winning Number is 8
0.45
0.32
0.02
0.12E-23
0.12E-125
0.99
10 outputs
M hidden
N inputs
10
Net Activation
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Back Propagation
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Rejection

• It is better to reject a digit than to classify
  it incorrectly.
• To avoid rejection, the results must meet
  criteria
• Highest confidence value must be greater than
  87.5
• This value must be at least 20 more than he
  second-highest value

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Trials Results

• Edge Detection total failure
• Edge maps looked nice, but weren't useful
• Bad news The networks trained over the edge maps
  were horribly innacurate
• Good news The network trained over the simple
  scaled image proved nearly as accurate

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More Trials Results

• Different scaled image sizes
• 16x16
• 8x8
• 4x4
• Goal find best performance, possibly by taking a
  vote from more than one network.
• Results the 16x16 and 8x8 networks are too
  large/slow to effectively train and use. The 4x4
  is roughly gt90 accurate with lt15 rejection.

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Results (32x32 down-sampled to 4x4-3-bit)
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References

• 1 T. Bruel. Segmentation of Handprinted
  Letter Strings using a Dynamic Programming
  Algorithm. Presented at the Sixth International
  Conference on Document Analysis and Recognition
  (ICDAR 01), September 1991.
• 2 L. Fontaine, L. Shastri. Handprinted Digit
  Recognition Using Spatiotemporal Connectionist
  Models. Technical Report MS-CIS-92-24,
  University of Pennsylvania, March 1992.
• 3 D. C. Alvarez, F.M. Rodriguez, X.F. Hermida.
  Printed and Handwritten Digits Recognition Using
  Neural Networks. Original publication source
  unkown paper available at http//wgpi.tsc.uvigo.e
  s/pub/papers/icsp98_1.pdf
• 4 Y. Le Cun. A Theoretical Framework for
  Back-Propagation. From proceedings of 1998
  Connectionist Models Summer School, 21-28.
• 5 Y. Le Cun, B. Boser, J.S. Denker, D.
  Henderson, R.E. Howard, W. Hubbard, L.D. Jackel.
  Handwritten Digit Recognition with a
  Back-Propagation Neural Network. Advances in
  Neural Information Processing Systems, Vol. 2,
  598-605. Morgan Kaufmann, 1990.
• 6 O. Matan, C. J. C. Burges, Y. Le Cun, J. S.
  Denker. Multi-Digit Recognition Using A Space
  Displacement Neural Network. Advances in Neural
  Information Processing Systems, Vol. 4, 488-495.
  Morgan Kaufmann, 1992.
• 7 G. Velasquez, A Distributed Approach to a
  Neural Network Simulation Program'.' Master's
  thesis, The University of Texas at El Paso, El
  Paso, TX, 1998