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 pre-processing, normalization, network establishment and recognition. Pre-processing involves digitization, noise removal and boundary detection. After boundary detection, the input character matrix is normalized into 12×8 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% accuracy only for numeric digits (0~9), 98% accuracy only for letters (A~Z) and more than 94% accuracy for alphanumeric characters by considering inter-class similarity measurement.