Handwritten English Character Recognition Based on Artificial Neural Network with Feature Extraction

Parag Narendra Achaliya, Sonal Patil


Handwriting Recognition is one of the energetic and challenging research areas in image processing and pattern recognition. It has a number of applications which include; reading support for blind people, bank cheques, reading government documents, reading documents in schools/colleges, etc. In this application, an effort is made to recognize handwritten characters for English alphabets with feature extraction using Multilayer Feed Forward Neural Network. Each character data set contains 26 alphabets and primarily 2 symbols viz. full stop & comma. 50 diverse character data sets are used for training the neural network. In the proposed system, each character is resized into 30x20 pixels,which will directly be used for training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network

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