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Synthetic Data Generation and Shuffled Multi-Round Training Based Offline handwritten mathematical expression recognition

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Journal of Computer Science & Technology 2022年第6期37卷 1427-1443页

作者： Lan-Fang Dong Han-Chao Liu Xin-Ming Zhang School of Computer Science and Technology University of Science and Technology of ChinaHefei 230022China

Offline handwritten mathematical expression recognition is a challenging optical character recognition(OCR)task due to various ambiguities of handwritten symbols and complicated two-dimensional *** work in this area usually constructs deeper and deeper neural networks trained with end-to-end approaches to improve the ***,the higher the complexity of the network,the more the computing resources and time *** improve the performance without more computing requirements,we concentrate on the training data and the training strategy in this *** propose a data augmentation method which can generate synthetic samples with new LaTeX notations by only using the official training data of ***,we propose a novel training strategy called Shuffled Multi-Round Training(SMRT)to regularize the *** the generated data and the shuffled multi-round training strategy,we achieve the state-of-the-art result in expression accuracy,i.e.,59.74%and 61.57%on CROHME 2014 and 2016,respectively,by using attention-based encoder-decoder models for offline handwritten mathematical expression recognition.

关键词： handwritten mathematical expression recognition offline synthetic data generation training strategy

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Robust encoder–decoder learning framework for offline handwritten mathematical expression recognition based on a multi-scale deep neural network

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Science China(Information Sciences) 2021年第3期64卷 220-222页

作者： Guangcun SHAN Hongyu WANG Wei LIANG Kai CHEN School of Instrumentation Science and Optoelectronics Engineering Beihang University Institute of Electronics Chinese Academy of Sciences School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences Boheng Technology (Hangzhou) Co.Ltd

Dear editor,mathematical expressions have been widely employed in scientific research, finance, and statistics, and play a significant role in educational activities. For example, if a computer can recognize teachers’ handwritten expressions as standard printed mathematical expressions, this will undoubtedly be more conducive and helpful for improving the effectiveness of lectures. Thus, the question of how to make computers automatically recognize mathematical expressions is highly significant.

关键词： Encoder-Decoder Multi-Scale Attention handwritten mathematical expression recognition Attention recurrent neural network Dense Network

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