@inproceedings{93954a719f41430b911445f917efc882,
title = "Improving off-line handwritten chinese character recognition with semantic information",
abstract = "Off-line handwritten Chinese character recognition (HCCR) is a well-developed area in computer vision. However, existing methods only discuss the image-level information. Chinese character is a kind of ideograph, which means it is not only a symbol indicating the pronunciation but also has semantic information in its structure. Many Chinese characters are similar in writing but different in semantics. In this paper, we add semantic information into a two-level recognition system. First we use a residual network to extract image features and make a premier prediction, then transform the image features into a semantic space to conduct a second prediction if the confidence of the previous prediction is lower than a threshold. To the best of our knowledge, we are the first to introduce semantic information into Chinese handwritten character recognition task. The results on ICDAR-2013 off-line HCCR competition dataset show that it is meaningful to add semantic information to HCCR.",
keywords = "Character embedding, Handwritten Chinese character recognition, Semantic information",
author = "Hongjian Zhan and Shujing Lyu and Yue Lu",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 25th International Conference on Neural Information Processing, ICONIP 2018 ; Conference date: 13-12-2018 Through 16-12-2018",
year = "2018",
doi = "10.1007/978-3-030-04221-9\_47",
language = "英语",
isbn = "9783030042202",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "528--536",
editor = "Leung, \{Andrew Chi Sing\} and Long Cheng and Seiichi Ozawa",
booktitle = "Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings",
address = "德国",
}