发布时间：

2026-06-30

所属单位：

人工智能教育学部

论文名称：

An Auto-Grading Oriented Approach for Off-Line Handwritten Organic Cyclic Compound Structure Formulas Recognition

发表刊物：

Computer Modeling in Engineering & Sciences

刊物所在地：

国外学术期刊

关键字：

Handwritten chemical structure formulas; structure interpretation; components detection; text recognition

摘要：

Auto-grading, as an instruction tool, could reduce teachers' workload, provide students with instant feedback and support highly personalized learning. Therefore, this topic attracts considerable attentions from researchers recently. To realize the automatic grading of handwritten chemistry assignments, the problem of chemical notations recognition should be solved first. The recent handwritten chemical notations recognition solutions belonging to the end-to-end trainable category suffered fromthe problem of lacking the accurate alignment information between the input and output. They serve the aim of reading notations into electrical devices to better prepare relevant e-documents instead of auto-grading handwritten assignments. To tackle this limitation to enable the auto-grading of handwritten chemistry assignments at a fine-grained level. In this work, we propose a component-detection-based approach for recognizing off-line handwritten Organic Cyclic Compound Structure Formulas (OCCSFs). Specifically, we define different components of OCCSFs as objects (including graphical objects and text objects), and adopt the deep learning detector to detect them. Then, regarding the detected text objects, we introduce an improved attention-based encoder-decoder model for text recognition. Finally, with these detection results and the geometric relationships of detected objects, this article designs a holistic algorithm for interpreting the spatial structure of handwritten OCCSFs. The proposedmethod is evaluated on a self-collected data set consisting of 3000 samples and achieves promising results.

合写作者：

Wang, Yifei,Jin, Xinxin,Gu, Zhiwen,Zhang, Xiaoliang

第一作者：

张婷

论文类型：

期刊论文

通讯作者：

何彬

论文编号：

1722800746069626880

文献类型：

00001

卷号：

135

期号：

3

页面范围：

2267-2285

ISSN号：

1526-1492

是否译文：

否

发表时间：

2023-06-01

收录刊物：

其他（ESI、Scopus、OA）、EI、SCI