1. Neural Network Applications in OCR Daniel Hentschel Robert Johnston Center for Imaging Science Rochester Institute of Technology

2. Background Recently, many Ancient documents have been discovered. Deciphering these documents is integral to understanding our past and predicting our future as a human race.

3. Background Unfortunately, the majority of ancient Hebrew documents found are seriously degraded.

4. Background This degradation makes deciphering of the documents difficult. Many techniques have been developed to enhance the text in degraded manuscripts in order to improve readability. The majority of this work has been carried out by Roger Easton and Robert Johnston at the Center for Imaging Science at RIT. Note:

5. Question: Would it be beneficial to use a neural network to help in the deciphering of degraded ancient Hebrew documents?

6. Research Purpose Create an application to assist in document restoration. Develop a neural network adept at Hebrew OCR (optical character recognition). Analyze the functionality of the network when studying degraded characters

7. Creating an Application

8. Developed with Visual Basic

9. Calculate Input Features Segmentation. Horizontal and Vertical section averages. Length of the skeleton as a percentage of the circumscribed rectangle perimeter. Complexity: Square root of the black area divided by the length of the skeleton. Number of dead ends and intersections in the skeleton of the character.

10. Segmentation Simple segmentation. Not elegant, but functional.

11. Thinning Algorithm Adapted from “A Fast Thinning Algorithm For Characters” (Flores, Rezende, Carrijo, Yabu-tti) Red - pixel being analyzed Green/Blue - to be deleted

12. Workings of a Neural Network Input Layer Hidden Layer Output Layer

13. Nodes in a Neural Network Inputs are multiplied by a weighting factor and a bias is added. Weighted inputs are summed. Sum is applied to a function:

14. Internal Function of a Node

15. Operation of a Node

16. Input to the Neural Network 9 input nodes –Horizontal (3) and Vertical (2) histogram. –Length of the skeleton as a percentage of the circumscribed rectangle perimeter. –Complexity: Square root of the black area divided by the length of the skeleton. –Number of changes of intersections, and dead ends encountered in the skeleton.

17. Output of the Neural Network 4 output nodes

18. Hidden Nodes 3 hidden nodes works well for 4 characters

19. Results Training set:

20. Results Characters not in training set:.07.00.94.04.05.92.05.08.04.03.94.07.95.02.86.05.02.08.04.92.12.77.01.13

21. Conclusion The neural network is quite effective at deciphering non-degraded text. Not enough degraded characters have been studied yet to determine how well the network will perform Future work: –More than 4 characters –Optimize inputs –Analyze degraded characters

22. The End