Handwritten Amazigh Character Recognition Based On Hidden Markov Models

In this article we describe a simple, global approach to the recognition of handwritten Amazigh characters. The method used is based on Hidden Markov Models. The input provided to our system is a vector of features extracted directly from an image of Amazigh characters. This vector is translated into sequence of observations that is used for the learning phase. This task involves several of processing steps, typically, pre-processing and feature extraction. Finally the class of the input characters is determined by the Forword classifier. The obtained results are promising.