首页|Multimodal Dependence Attention and Large-Scale Data Based Offline Handwritten Formula Recognition
Multimodal Dependence Attention and Large-Scale Data Based Offline Handwritten Formula Recognition
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万方数据
维普
Offline handwritten formula recognition is a challenging task due to the variety of handwritten symbols and two-dimensional formula structures.Recently,the deep neural network recognizers based on the encoder-decoder frame-work have achieved great improvements on this task.However,the unsatisfactory recognition performance for formulas with long IATEX strings is one shortcoming of the existing work.Moreover,lacking sufficient training data also limits the capability of these recognizers.In this paper,we design a multimodal dependence attention(MDA)module to help the model learn visual and semantic dependencies among symbols in the same formula to improve the recognition perfor-mance of the formulas with longLATEXstrings.To alleviate overfitting and further improve the recognition performance,we also propose a new dataset,Handwritten Formula Image Dataset(HFID),which contains 25 620 handwritten formula images collected from real life.We conduct extensive experiments to demonstrate the effectiveness of our proposed MDA module and HFID dataset and achieve state-of-the-art performances,63.79％and 65.24％expression accuracy on CROHME 2014 and CROHME 2016,respectively.
handwritten formula recognitionmultimodal dependence attentionsemantic dependencevisual depen-denceHandwritten Formula Image Dataset
刘汉超、董兰芳、张信明
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School of Computer Science and Technology,University of Science and Technology of China,Hefei 230022,China
National Key Research and Development Program of China
万方数据
维普
