Artificial neural networks are commonly used in optical character recognition algorithms due to their flexibility, ability to learn, and power. ANNs work by taking an input, running it through a network of neurons arranged in layers, and producing an output. They can be trained to recognize patterns through a learning stage where they are given many examples of input and output pairs. Once trained, ANNs can accurately evaluate new inputs and recognize characters at a 98% rate with only 5% error. Common types of ANNs include feedforward, recurrent, radial basis function, and self-organizing networks.

1. OCR Algorithms Jacek Bajor

2. OCR Optical Character Recognition Translation of printed or handwritten text to digital form KAZ 813

3. OCR Fields Pattern Recognition Artificial Intelligence Machine Learning Computer Vision

4. Applications Digitalising libraries

5. Applications books.google.com

6. Applications Office work

7. Applications Captchas Mr. blocked

8. Applications Recognising licence plates And many, many others...

9. OCR Algorithms Artificial Neural Networks

10. Why ANN?

11. Why ANN? They are flexible They can be thought They can learn themselves They are powerful

12. ANNs Let’s assume ANN is a black magical box

13. ANNs Learning stage input output a a a a a a

14. ANNs Evaluation 98% a 5% a?

15. ANN structure Neuron (nerve cell)

16. ANN structure

17. ANN structure

18. ANNs types Feedforward Recurrent (back-propagation) Radial basis function network Kohonen self-organizing network Others...

19. ANNs applications Optical Character Recognition Function approximation Artificial Intelligence Finance Speech analysis Many, many others

20. Advantages Flexible Efficient if well designed

21. Disadvantages Focused on one activity Give approximations Inefficient if poorly designed

22. Libraries FANN - Fast Artificial Neural Network Library - leenissen.dk/fann Annie - annie.sourceforge.net Libann - savannah.nongnu.org/projects/libann

23. Thank you