Research Article
Study the Preprocessing Effect on RNN Based Online Uyghur Handwriting Word Recognition
@INPROCEEDINGS{10.1007/978-3-030-32216-8_43, author={Wujiahemaiti Simayi and Mayire Ibrayim and Askar Hamdulla}, title={Study the Preprocessing Effect on RNN Based Online Uyghur Handwriting Word Recognition}, proceedings={Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8--10, 2019, Proceedings}, proceedings_a={SIMUTOOLS}, year={2019}, month={10}, keywords={Online handwriting recognition Preprocessing Input representation Recurrent neural networks Connectionist temporal classification Uyghur words}, doi={10.1007/978-3-030-32216-8_43} }- Wujiahemaiti Simayi
Mayire Ibrayim
Askar Hamdulla
Year: 2019
Study the Preprocessing Effect on RNN Based Online Uyghur Handwriting Word Recognition
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-32216-8_43
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.
