Neural Network Classifiers for Off-Line Optical Handwritten Amazighe Character Recognition
Recognizing Amazighe characters is a difficult task in the area of Optical Character Recognition (OCR). This paper describes a new hybrid Amazighe character recognition system based on an artificial neural network classifier using Legendre moments without any preprocessing. The features extraction stage uses a set of moment descriptors which are invariants under shift and scaling. The actual classification is done using a multilayer perceptron network; with learning algorithm to generate a near optimal feed forward neural networks dynamically for the task of object recognition.