Online Arabic Handwriting Recognition

Abstract

This thesis presents a novel system approach of online Arabic handwriting recognition as well as datasets of online Arabic handwriting. To fill the gap and shortage of standard online Arabic handwriting datasets, the thesis introduces two datasets of this handwriting. The XML representation is selected for the datasets design. The construction of the datasets is achieved by developing two software tools (collection tool, verification tool) and then using these tools to collect and maintain online Arabic handwriting for the datasets.Therefore, we have acquired standard datasets which can be used by researchers to train and test their recognition techniques for this handwriting. These acquired datasets are used in the training and testing of the system approach which developed in this thesis. This system involves a position invariant feature extraction method. Moreover, it involves an algorithm which specifically designed in this thesis to divide the given cursive words of the handwriting into segments of Arabic letters. To fulfill the recognition, the system employs hidden Markov models so as to model the handwriting.The experiments which performed to evaluate the system have shown that the system performance is influenced by the feature arrangements within those feature vectors of the letter segments; such that the well done choice of the arrangement has enhanced the performance.