发布时间：

2026-06-30

所属单位：

人工智能教育学部

论文名称：

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

发表刊物：

Proceedings of the International Conference on Document Analysis and Recognition. International Conference on Document Analysis and Recognition

刊物所在地：

国内外公开发行

关键字：

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

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.

合写作者：

余新国

第一作者：

Zheng, Lina

论文类型：

会议论文

通讯作者：

张婷

论文编号：

1571044199434948608

文献类型：

00013

页面范围：

165-168

ISSN号：

1520-5363

是否译文：

否

发表时间：

2019-09-22

收录刊物：

其他（ESI、Scopus、OA）、EI