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Character Recognition Using Neural Networks

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.

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Character Recognition Using Neural Networks

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  1. Character Recognition Using Neural Networks EE 368 Semester Project Randy Dimmett

  2. Purpose Use a neural network to recognize text in a scanned image

  3. Procedure Used Courier New font for sample data and targets Develop network Test with ideal input Test with non-ideal input

  4. Procedure Generation of all letters in Courier New 12 pt. 27 inputs each having 108 attributes

  5. Procedure Ideal test data Non-Ideal data

  6. Tested Neural Networks Linear Associator using Pseudoinverse Rule Up to 9% Accuracy (25% if including spaces) THE ONLY GOOD DAY OF SCHOOL IS THE LAST ONE MLE RBNV FRRM M?Y PN ZBLQPO KZ OJJ LFFU LGN

  7. Tested Neural Networks 4-Layers using Back-propagation(2,5,2,and 1 neurons) Reached minimum MSE of .01 Very, Very Bad Results.

  8. Tested Neural Networks 5-Layers using Back-propagation(2,5,5,5, and 1 neurons) Reached MSE of about 0. Accuracy less than 6% THE ONLY GOOD DAY OF SCHOOL IS THE LAST ONE YBJ RACS AVST SAZ UZ YEGQRD ZW ZBH GAAZ SBF

  9. Troubleshooting Problems with data: Noisy Off-Set Effect

  10. Troubleshooting: Adding Noise With noise added to sample data, Linear Associator gives 12% accuracy THE ONLY GOOD DAY OF SCHOOL IS THE LAST ONE XLT OHPZ SIKD CGY BP KPNDCQ OQ ?JM KKLS KNP

  11. Troubleshooting: Getting Data Using sample data gotten from scanner, the Linear Associator gives 21% accuracy THE ONLY GOOD DAY OF SCHOOL IS THE LAST ONE QHP ONJV OLOD E?S NP KOEKNP JT SDN LFEO LNG

  12. Summary of Results

  13. Conclusions Character Recognition is not a good pattern recognition problem. Results depend greatly on the sample data used.

  14. Questions?

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