Text-independent Writer Identification on Online Arabic Handwriting

Most of existing works on online text-independent writer identification follow analytical approach based on grapheme or character as primitive. However, segmentation is time-consuming and becomes an obstacle in real time application systems. To avoid this problem, we propose a novel framework which follows global approach based on word as primitive. Different sets of statistic and dynamic features are used in different levels in the word. Features are extracted from the point, the stroke, the space between strokes and the whole word. In decision phase, Dynamic Time Warping (DTW) and Support Vector Machine (SVM) are used. To evaluate our framework, we use set 1 from the ADAB database (Arabic DAtaBase). A limited amount of data is available for some writers. In this paper, we focus on the effect of writers' number and words' number per writer on the obtained results. We highlight the accuracy of studied features and used classifiers. Experimental results are promising and show that our proposed framework can improve the identification rates.