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An end-to-end trainable system for offline handwritten chemical formulae recognition

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成果类型:
期刊论文
作者:
Liu, Xiaoxue;Zhang, Ting;Yu, Xinguo
通讯作者:
Zhang, Ting(ting.zhang@mail.ccnu.edu.cn)
作者机构:
[Liu, Xiaoxue] Central China Normal University Wollongong Joint Institute, Central China Normal University, China
[Zhang, Ting; Yu, Xinguo] National Engineering Research Center for E-learning, Central China Normal University, China
语种:
英文
期刊:
Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN:
1520-5363
年:
2019
页码:
577-582
机构署名:
本校为第一机构
院系归属:
国家数字化学习工程技术研究中心
伍伦贡联合研究院
摘要:
In this paper, we propose an end-to-end trainable system for recognizing handwritten chemical formulae. This system recognize once a time a chemical formula, instead of one chemical symbol or a whole chemical equation, which is in line with people's writing habits, at the same time could help to develop methods for the complicated chemical equations recognition. The proposed system adopts the CNN+RNN+CTC framework, which is one of state of the art methods in imagebased sequence labelling tasks. We extend the capability of the CNN+RNN+CTC framew...

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