NEURAL NETWORK-BASED ENGLISH ALPHANUMERIC CHARACTER RECOGNITION
Propose a neural-network based size and color invariant character recognition system using feed-forward neural network. Our feed-forward network has two layers. One is input layer and another is output layer. The whole recognition process is divided into four basic steps such as preprocessing, normalization, network establishment and recognition. Preprocessing involves digitization, noise removal and boundary detection. After boundary detection, the input character matrix is normalized into matrix for size invariant recognition and fed into the proposed network which consists of 96 input and 36 output neurons. Then we trained our network by proposed training algorithm in a supervised manner and established the network by adjusting weights. Finally, we have tested our network by more than 20 samples per character on average and give 99.99 percent accuracy only for numeric digits (0 to 9), 98 percent accuracy only for letters (A to Z) and more than 94 percent accuracy for alphanumeric characters by considering inter-class similarity measurement.
English Alphanumeric Character, Feed-forward neural network, Supervised Learning, weight-matrix, Character Recognition.