Character Recognition
Using Neural Networks
EE 368 Semester Project
Randy Dimmett
Purpose
Use a neural network to recognize text in a
scanned image
Procedure
Used Courier New font for sample data and
targets
Develop network
Test with ideal input
Test with non-ideal input
Procedure
Generation of all letters in Courier New 12 pt.
27 inputs each having 108 attributes
Procedure
Ideal test data
Non-Ideal data
Tested Neural Networks
Linear Associator using Pseudoinverse Rule
Up to 9% Accuracy (25% if including spaces)
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Tested Neural Networks
4-Layers using Back-propagation
(2,5,2,and 1 neurons)
Reached minimum MSE of .01
Very, Very Bad Results.
Tested Neural Networks
5-Layers using Back-propagation
(2,5,5,5, and 1 neurons)
Reached MSE of about 0.
Accuracy less than 6%
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Troubleshooting
Problems with data:
Noisy
Off-Set Effect
Troubleshooting: Adding Noise
With noise added to sample data,
Linear Associator gives 12% accuracy
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Troubleshooting: Getting Data
Using sample data gotten from scanner, the
Linear Associator gives 21% accuracy
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Summary of Results
%Accuracy
40
35
30
25
20
15
10
5
0
Character Recognition
Accuracy
Character Recognition
Accuracy incl. Spaces
With Ideal
Samples
With Noisy
Samples
With Samples
from Data
Source
Conclusions
Character Recognition is not a good pattern
recognition problem.
Results depend greatly on the sample data
used.
Questions?