Proc. SPIE. 10341, Ninth International Conference on Machine Vision (ICMV 2016)
KEYWORDS: Magnesium, Visual process modeling, Data modeling, Databases, Feature extraction, Personal digital assistants, Neural networks, Optical character recognition, Performance modeling, Prototyping
This paper highlights a new method for online Arabic handwriting recognition based on graphemes segmentation. The main contribution of our work is to explore the utility of Beta-elliptic model in segmentation and features extraction for online handwriting recognition. Indeed, our method consists in decomposing the input signal into continuous part called graphemes based on Beta-Elliptical model, and classify them according to their position in the pseudo-word. The segmented graphemes are then described by the combination of geometric features and trajectory shape modeling. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results using the benchmarking ADAB Database show the performance of the proposed method.