Generating realistic Arabic handwriting dataset - I. Abdalla, Mahmoud; A. Rashwan, Mohsen; A. Elserafy, Mohamed (International Journal of Engineering and Technology, 2019)
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 sample words ‎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 process 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 reproduction of new letters ‎shapes by using ‎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.