Recognition of Handwritten Chemical Organic Ring Structure Symbols Using Convolutional Neural Networks

被引：8

作者：

Zheng, Lina ^{[1
]}

Zhang, Ting ^{[2
]}

Yu, Xinguo ^{[2
]}

机构：

[1] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Hubei, Peoples R China

[2] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China

来源：

2019 INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION WORKSHOPS (ICDARW), VOL 5 | 2019年

基金：

中国博士后科学基金;

关键词：

Handwritten symbol recognition; Chemical organic ring structure symbols; convolutional neural networks;

D O I：

10.1109/ICDARW.2019.40099

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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 offline handwritten chemical ORS Symbols. To evaluate it, a new dataset of 3600 samples are collected of which 90% is for training while 10% is for test. The recognition accuracy is 84.3% with VGGNet-16 and 92.4% with VGGNet-19.

引用

页码：165 / 168

页数：4

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