During the previous year's holistic approach showing satisfactory results to solve the problem of Arabic handwriting word recognition instead of word letters segmentation. In this paper, we present an efficient system for generation realistic Arabic handwriting dataset from ASCII input text. We carefully selected simple word list that contains most Arabic letters normal and ligature connection cases. To improve the performance of new letters reproduction we developed our normalization method that adapt its clustering action according to created Arabic letters families. We enhanced Gaussian Mixture Model process to learn letters template by detecting the number and position of Gaussian component by implementing Ramer-Douglas-Peucker algorithm which improve the new letters shapes reproduced by using and Gaussian Mixture Regression. We learn the translation distance between word-part to achieve real handwriting word generation shape. Using combination of LSTM and CTC layer as a recognizer to validate the efficiency of our approach in generating new realistic Arabic handwriting words inherit user handwriting style as shown by the experimental results.
Semiparametric mixture regression with unspecified error distributions
