Recognition of Offline Handwritten Arabic Words Using a Few Structural Features

Handwriting recognition is one of the most significant problems in pattern recognition, many studies have been proposed to improve this recognition of handwritten text for different languages. Yet, Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts. The present paper suggests a feature extraction technique for offline Arabic handwriting recognition. A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function (RBF) neural networks is proposed. The methods of feature extraction are central to achieve high recognition performance. The proposed methodology relies on a feature extraction technique based on many structural characteristics extracted from the word skeleton (subwords, diacritics, loops, ascenders, and descenders). In order to reach our purpose, we built our own word database and the proposed system has been successfully tested on a handwriting database of Algerian city names (wilayas). Finally, a simple classifier based on the radial basis function neural network is presented to recognize certain words to verify the reliability of the proposed feature extraction. The experiments on some images of the benchmark IFN/ENIT database show that the proposed system improves recognition and the results obtained are indicative of the efficiency of our technique.