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Hand Written Recognition Using Neural Network Algorithm | ||
| Journal of Engineering | ||
| Article 1, Volume 16, Issue 4, 2010, Pages 0-0 | ||
| Author | ||
| Ammar O. Hoori | ||
| Abstract | ||
| Hand written recognition problem can be done in two major steps, first by separating each character alone and second by detecting the separated shape to its corresponding like alphabetic letter. A backpropagation neural network found to be a good artificial intelligence algorithm in facing character recognition problem. In this work, backpropagation neural network is used with 3-layers to detect and separate 26 English letter from (A to Z). In addition, a previous steps should be taken to detect the boundaries of each single written letter. Detecting a complete text can be done by separating each character through finding its boundaries, resizing the separated character to be suitable for pre-trained neural network, detecting the hand-written letter and finally saving the guessed letter to a text file. This work is developed using Matlab 2008 version 7.6. The obtained results show good representations of letter contaminated by noise and non-trained letters. | ||
| Keywords | ||
| Character Recognition; neural network; artificial intelligence; Backpropogation; Hand written recognition | ||
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