Online Persian/Arabic script classification without contextual information

Abstract

Segmentation accuracy plays a vital role in script recognition process and therefore, research in this area is still fresh. Accordingly, this paper presents a new method for segmenting cursive handwritten Persian/Arabic words. Moreover, this paper proposes a novel strategy to tackle the challenge of recognising the handwritten sub-word as input without contextual information. The recognition strategy is tested with Persian scripts but can be used for both Person and Arabic scripts. Initially, the input data are pre-processed to generate desired data. Later, the proposed segmentation approach fragments the pre-processed data at regular intervals. The discriminative features are extracted as a binary input set for a hybrid of artificial neural networks and particle swarm optimisation (ANN–PSO) classifier to recognize Persian script. Finally, the achieved segmentation and recognition rates are compared with the results reported in the literature. Segmentation and recognition rates thus achieved exhibit promising achievements in the state of art.

Keywords:

• Persian script segmentation
• Recognition
• Features extraction
• Pre-processing
• Contextual information

Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no. RGP-264.