Cursive multilingual characters recognition based on hard geometric features

The cursive nature of Arabic script characters segmentation and recognition has attracted researchers from academia and industry. However, despite several decades of research, still Arabic characters classification accuracy is not up to the mark. This paper presents an automated approach for Arabic characters segmentation and recognition. The proposed methodology explores character's boundaries based on their geometric features, prior to their recognition. However, due to uncertainty and without dictionary support few characters are over-divided. To expand the productivity of the proposed methodology a hybrid neural model (HNN) is proposed. The model is composed of trained MLP and RBF networks to assist the character's boundary recognition process. For reasonable examination, only benchmark dataset is utilised.