The document discusses optical character recognition (OCR) using neural networks. It provides an overview of OCR and how it works to convert scanned images of text into machine-encoded text. It then describes how neural networks can be used for OCR by training a network to identify letters and characters from input images. The document outlines the basic structure of neural networks and how they can be configured and trained for the purpose of OCR.

1. OCR with Neural Network Made By: Marwa Fadhel Jassim Karam Samir Khalid

2. Introduction Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files, to computerize a record-keeping system in an office, or to publish the text on a website.

4. programmed with images of each character, and worked on one font at a time. "Intelligent" systems with a high degree of recognition accuracy for most fonts are now common.Some systems are capable ofreproducing formatted output that closely approximates the original scanned page including images, columns and other non-textual components.

6. OCR Step 1: Find letters

11. Real Neurons

12. Neuronal Connections

13. Firing neurons excite others Firing threshold

14. Firing neurons excite others Firing threshold

15. Firing neurons excite others Firing threshold

16. ...which in turn excite others Firing threshold

17. Inputs can be weighted Firing threshold 0.7 0.4

18. Neurons can suppress others Firing threshold 0.7 0.4 -0.5

19. And they can have a starting bias Firing threshold Bias 0.3

20. (So they're basically logic gates) 0.5 0.5 1 1 -1 Bias: 1 AND OR NOT

23. Training Method The most popular and simple approach to OCR problem is based on feed forward neural network with back propagation learning. The main idea is that we should first prepare a training set and then train a neural network to recognize patterns from the training set. In the training step we teach the network to respond with desired output for a specified input. For this purpose each training sample is represented by two components: possible input and the desired network's output for the input. After the training step is done, we can give an arbitrary input to the network and the network will form an output, from which we can resolve a pattern type presented to the network.

24. Let's assume that we want to train a network to recognize 26 capital letters represented as images of 5x6 pixels, something like this one: One of the most obvious ways to convert an image to an input part of a training sample is to create a vector of size 30 (for our case), containing "1" in all positions corresponding to the letter pixel and "0" in all positions corresponding to the background pixels. But, in many neural network training tasks, it's preferred to represent training patterns in so called "bipolar" way, placing into input vector "0.5" instead of "1" and "-0.5" instead of "0". Such sort of pattern coding will lead to a greater learning performance improvement.

25. our training sample should look something like this: For each possible input we need to create a desired network's output to complete the training samples. For OCR task it's very common to code each pattern as a vector of size 26 (because we have 26 different letters), placing into the vector "0.5" for positions corresponding to the patterns type number and "-0.5" for all other positions

26. So, a desired output vector for letter "K“ will look something like this: After having such training samples for all letters, we can start to train our network. But, the last question is about the network's structure. For the above task we can use one layer of neural network, which will have 30 inputs corresponding to the size of input vector and 26 neurons in the layer corresponding to the size of the output vector.

27. The OCR software breaks the image into sub-images, each containing a single character. The sub-images are then translated from an image format into a binary format, where each 0 and 1 represents an individual pixel of the sub-image. The binary data is then fed into a neural network that has been trained to make the association between the character image data and a numeric value that corresponds to the character. The output from the neural network is then translated into ASCII text and saved as a file. Another Method

29. Thanks for listening