Neural Network-Based English Alphanumeric Character Recognition
Md. Fazlul Kader · International Journal of Computer Science Engineering and Applications · 2012
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.