Incorporating a new relational feature in Arabic online handwritten character recognition

Artificial neural networks have shown good performance in classification tasks. However, models used for learning in pattern classification are challenged when the differences between the patterns of the training set are small. Therefore, the choice of effective features is mandatory for obtaining good performance. Statistical and geometrical features alone are not suitable for recognition of hand printed characters due to variations in writing styles that may result in deformations of character shapes. We address this problem by using a relational context feature combined with a local descriptor for training a neural network-based recognition system in a user-independent online character recognition application. Our feature extraction approach provides a rich representation of the global shape characteristics, in a considerably compact form. This new relational feature provides a higher distinctiveness and increases robustness with respect to character deformations. While enhancing the recognition accuracy, the feature extraction is computationally simple. We show that the ability to discriminate in Arabic handwriting characters is increased by adopting this mechanism in feed forward neural network architecture. Our experiments on Arabic character recognition show comparable results with the state-of-the-art methods for online recognition of these characters.