[Zheng, Lina] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Hubei, Peoples R China.
[Yu, Xinguo; Zhang, Ting] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
通讯机构：
[Zhang, Ting] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
语种：
英文
关键词：
Handwritten symbol recognition;Chemical organic ring structure symbols;convolutional neural networks
期刊：
2019 INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION WORKSHOPS (ICDARW), VOL 5
ISSN：
1520-5363
年：
2019
页码：
165-168
会议名称：
15th IAPR International Conference on Document Analysis and Recognition (ICDAR) / 2nd Workshop of Machine Learning (WML)
会议论文集名称：
Proceedings of the International Conference on Document Analysis and Recognition
会议时间：
SEP 21-22, 2019
会议地点：
Sydney, AUSTRALIA
会议主办单位：
[Zheng, Lina] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Hubei, Peoples R China.^[Zhang, Ting;Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2019M652678]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [CCNU18XJ046]
机构署名：
本校为第一且通讯机构
院系归属：
国家数字化学习工程技术研究中心
摘要：
Many types of data exhibit characteristic of rotational symmetry. Chemical Organic Ring Structure(ORS) Symbol is such a case. In this paper, we focus on offline handwritten chemical ORS Symbols recognition using convolutional neural networks(CNNs), from application point of view, in order to relax the inconvenience and ineffectiveness of the traditional click-and-drag style of interaction when input chemical notations into electronic devices; from scientific point of view, to explore the capacity of rotation invariance of CNNs using data augmentation. We propose a VGGNet-based classifier for o...
