A Time Delay Neural Network for Online Arabic Handwriting Recognition

Handwriting recognition is an interesting part in pattern recognition field. In the last decade, several approaches are focused on online handwriting recognition because the very rapid growth of new technologies in the field of data entry. In this paper, we propose a new system for online Arabic handwriting recognition based on beta-elliptic model which allow to segment the trajectory into segments called strokes by inspecting the extremums points of velocity profile and extract their dynamic and geometric profiles. These strokes are used to train the Time Delay Neural Network (TDNN) which is able to represent the sequential aspect of input data. To evaluate our method, we have used a total of 25000 Arabic letters from the LMCA database. Our experimental results demonstrate the effectiveness of our proposed method and show recognition rates exceeds the 95%.