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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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References

  1. Anderson R H. Syntax-directed recognition of hand-printed two-dimensional mathematics. In: Proceedings of the Association for Computing Machinery Inc. Symposium. New York: ACM, 1968. 436–459

    Google Scholar 

  2. Lavirotte S, Pottier L. Mathematical formula recognition using graph grammar. In: Proceedings of SPIE, 1998. 3305: 44–52

    Article  Google Scholar 

  3. Chan K F, Yeung D Y. Error detection, error correction and performance evaluation in online mathematical expression recognition. Pattern Recogn, 2001, 34: 1671–1684

    Article  Google Scholar 

  4. Yamamoto R. On-line recognition of handwritten mathematical expression based on stroke-based stochastic context-free grammar. In: Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition, La Baule, 2006

    Google Scholar 

  5. MacLean S, Labahn G. A new approach for recognizing handwritten mathematics using relational grammars and fuzzy sets. Int J Document Anal Recogn, 2013, 16: 139–163

    Article  Google Scholar 

  6. Huang G, Liu Z, Weinberger K Q. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 2261–2269

    Google Scholar 

  7. Cho K, Merrienboer B, Bahdanau D, et al. On the properties of neural machine translation: encoderdecoder approaches. 2014. ArXiv: 1409.1259

    Google Scholar 

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2016YFE0204200) and National 1000-Talent Youth Program. The authors want to thank Dr. Jianshu ZHANG for insightful comments and suggestions.

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Correspondence to Guangcun Shan.

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11432_2018_9824_MOESM1_ESM.pdf

Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network

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Shan, G., Wang, H., Liang, W. et al. Robust encoder-decoder learning framework for offline handwritten mathematical expression recognition based on a multi-scale deep neural network. Sci. China Inf. Sci. 64, 139101 (2021). https://doi.org/10.1007/s11432-018-9824-9

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  • DOI: https://doi.org/10.1007/s11432-018-9824-9

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