Abstract
There is little work done on unconstrained handwritten Uyghur word recognition by implementing deep neural networks. This paper carries out a comparative study to see the preprocessing effect on training a neural network based online handwriting Uyghur word recognition system. Bidirectional recurrent neural network with connectionist temporal classification is implemented for unconstrained handwriting word recognition experiments on a dataset of 23400 Uyghur word samples. The results are directly obtained from model output without any lexicon or language model. Experiments showed that proper preprocessing can improve the training speed very effectively. The comparative study conducted in this paper can be good reference for later studies.
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Acknowledgment
This work is supported by National Science Foundation of China (NSFC) under grant number 61462081 and 61263038. The first author is very much grateful to the National Laboratory of Pattern Recognition of CASIA for providing the experimental environment.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Simayi, W., Ibrayim, M., Hamdulla, A. (2019). Study the Preprocessing Effect on RNN Based Online Uyghur Handwriting Word Recognition. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-32216-8_43
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DOI: https://doi.org/10.1007/978-3-030-32216-8_43
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