Perceptual Recognition of Arabic Literal Amounts

Since humans are the best readers, one of the most promising trends in automatic handwriting recognition is to get inspiration from psychological reading models. The underlying idea is to derive benefits from studies of human reading, in order to build efficient automatic reading systems. In this context, we propose a human reading inspired system for the recognition of Arabic handwritten literal amounts. Our approach is based on the McClelland and Rumelhart’s neural model called IAM, which is one of the most referenced psychological reading models. In this article, we have adapted IAM to suit the Arabic writing characteristics, such as the natural existence of sub-words, and the particularities of the considered literal amounts vocabulary. The core of the proposed system is a neural network classifier with local knowledge representation, structured hierarchically into three levels: perceptual structural features, sub-words and words. In contrast to the classical neural networks, localist approach is more appropriate to our problem. Indeed, it introduces a priori knowledge which leads to a precise structure of the network and avoids the black box aspect as well as the learning phase. Our experimental recognition results are interesting and confirm our expectation that adapting human reading models is a promising issue in automatic handwritten word recognition.