Generating realistic Arabic handwriting dataset

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    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.‎

     


  • Keywords


    Arabic handwriting ; normalization; ligatures; template learning; Gaussian regression.

  • References


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Article ID: 29786
 
DOI: 10.14419/ijet.v8i4.29786




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