Contribution on Character Modelling for Handwritten Arabic Text Recognition

Arabic script is considered to be one of the most complex writing systems, which complicate the text recognition task. Among its complexities, the shape of the character depends according to its position in the word. More than 170 different shapes could be constructed to represent 28 basic letters; some of them are more used than others in the Arabic writing. To make training and recognition of characters more efficient, a study on shape modelling of different handwritten Arabic characters seems to be important. A segmentation-free word recognition system based on Hidden Markov Models (HMMs) is used to conduct this study. Experimental results are given for different sets of shape models using the IFN/ENIT database which contains an important number of handwritten Arabic words covering different writing styles.