发布时间：

2026-06-30

所属单位：

人工智能教育学部

论文名称：

A Component-detection-based Approach for Interpreting Off-line Handwritten Chemical Cyclic Compound Structures

发表刊物：

IEEE TALE2021: IEEE INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY AND EDUCATION

刊物所在地：

国内外公开发行

关键字：

Cyclic compound structure formulas; Handwriting recognition; Structure interpretation; Component detection

摘要：

Recently, off-line handwritten chemical structure formulas recognition draws considerable attention from researchers for its research challenges and supports to numerous interesting applications in education. The existing solutions belong to the rule-based category or the end-to-end trainable category, which suffer from the problems of low generalization capability or lacking the accurate alignment information between the input and output. To tackle these problems to enable auto correction of handwritten chemical assignments at a fine-grained level, in this paper we propose a component-detection-based approach for interpreting the spatial structure of off-line handwritten chemical cyclic compound structure formulas. Specifically, we define different components of cyclic compound structure formulas as objects (including graphical objects and text objects) and adopt the deep learning detector to detect them. Then, considering the property of chemical notations, we propose the Non-Maximum Area Suppression (NMAS) algorithm to improve the detection results. Finally, with these detection results and the geometric relationships of detected objects, this paper design a holistic algorithm for interpreting the spatial structure of handwritten cyclic compound structure formulas. The proposed method is evaluated on a self-collected dataset consisting 2100 samples and achieved an accuracy of 89.5%

合写作者：

余新国

第一作者：

Wang, Yifei

论文类型：

会议论文

通讯作者：

张婷

论文编号：

1575762469194960896

文献类型：

00013

页面范围：

785-791

是否译文：

否

发表时间：

2021-12-08

收录刊物：

其他（ESI、Scopus、OA）、EI