Research on the Recognition of Offline Handwritten New Tai Lue Characters Based on Bidirectional LSTM

DOI

10.2991/ncce-18.2018.189How to use a DOI?

Keywords

Offline handwritten character recognition, Bidirectional LSTM, New Tai Lue characters.

Abstract

Deep learning has made breakthrough progress in image recognition, target detection and tracking in recent years. It is proved too good at classification tasks. In this paper, we have compared use of Convolutional neural network(CNN), VGG16, Long Short-Term Memory (LSTM), and Bidirectional LSTM to perform offline handwriting New Tai Lue Characters recognition. These methods have been tested on a dataset build by our laboratory. For testing purpose 58795 samples including 9834 test samples of handwriting New Tai Lue Characters are used in these experiments. The experimental results show that the recognition rates are 91.23%, 89.33%, 92.78% for CNN, VGG16 and LSTM. Moreover, the best recognition result is obtained with the Bidirectional LSTM based method, whose recognition rate is 94.87% on the dataset.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yongqiang Wang
AU  - Pengfei Yu
AU  - Hongsong Li
AU  - Haiyan Li
PY  - 2018/05
DA  - 2018/05
TI  - Research on the Recognition of Offline Handwritten New Tai Lue Characters Based on Bidirectional LSTM
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 1115
EP  - 1123
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.189
DO  - 10.2991/ncce-18.2018.189
ID  - Wang2018/05
ER  -