A novel multilayer perceptron artificial neural network based recognition for Kurdish manuscript

Abstract

Alphabetic recognition is one of the most interesting and successful research fields in artificial intelligence and pattern recognition. The different writing structures of the languages and the presence of different approaches in diagnosis of letters of the different languages has been a challenge in alphabetic recognition. All these challenges have made many researches switch this research area. Of the different approaches in alphabetic recognition, the neural artificial networks could have been successful according to the capability of parallel process and learning capability for a special application like recognition pattern. So, it is a suitable approach in alphabet recognition. The Kurdish language holds two manuscripts according to Latin and Arabic alphabets. In this paper the Multilayer Perceptron (MLP) artificial neural networks are studied by the back-propagation algorithm to recognize the Kurdish-Latin manuscript. The proposed method is also applicative for diagnosis of all letters of the other Latin languages like English, Italian and etc. In this paper the MLP artificial neural networks are implemented in MATLAB environment. The efficiency factor for recognition of the Kurdish letters is to maximize the recognition accuracy of the Kurdish letters in training and testing the MLP artificial neural networks. This accuracy is 85.1535% in training stage and 81.2677% in testing stage.