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 â€Žthe â€Žproblem of Arabic handwriting wordÂ recognition instead of word letters â€Žâ€Žsegmentation.â€Ž â€ŽIn this paper, we present an efficient system for â€Ž generation realistic Arabic handwriting dataset from ASCII input â€Žtext. We carefully selected simple word list that contains most Arabic â€Žletters normal and ligature connection cases. To improve the â€Žperformance of new letters reproduction we developed our â€Žnormalization method that adapt its clustering action according to â€Žcreated Arabic letters families. We enhancedÂ Gaussian Mixture â€ŽModel process to learn letters template by detecting the â€Žnumber and position of Gaussian component by implementing â€ŽRamer-Douglas-Peuckerâ€Ž algorithm which improve the new letters â€Žshapes reproduced by using and Gaussian Mixture Regression. â€Žâ€ŽWe learn the translation distance between word-part to achieve â€Žreal handwriting word generation shape.â€Ž Using combination of LSTM and CTC layer as a recognizer to validate the â€Žefficiency of our approach in generating new realistic Arabic handwriting words inherit user handwriting style as shown by the experimental results.â€ŽÂ