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

作　　者：Ting Zhang Yifei Wang Xinxin Jin Zhiwen Gu Xiaoliang Zhang Bin He 

机构地区：[1]Faculty of Artificial Intelligence in Education,Central China Normal University,Wuhan,430079,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第6期2267-2285,共19页工程与科学中的计算机建模（英文）

基　　金：supported by National Natural Science Foundation of China (Nos.62007014 and 62177024);the Humanities and Social Sciences Youth Fund of the Ministry of Education (No.20YJC880024);China Post Doctoral Science Foundation (No.2019M652678);the Fundamental Research Funds for the Central Universities (No.CCNU20ZT019).

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 edocuments 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-detectionbased 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.

关 键 词：Handwritten chemical structure formulas structure interpretation components detection text recognition 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]