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J-GLOBAL ID:202102269389545329   Reference number:21A0423756

CNN and 2D BLSTM for Local Feature Extraction in Handwritten Mathematical Expression Recognition

手書き数式認識におけるCNNと2D BLSTMによる局所特徴抽出
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Volume: 120  Issue: 300(PRMU2020 38-68)  Page: 105-110 (WEB ONLY)  Publication year: Dec. 10, 2020 
JST Material Number: U2030A  ISSN: 2432-6380  Document type: Proceedings
Article type: 原著論文  Country of issue: Japan (JPN)  Language: JAPANESE (JA)
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Pattern recognition 
Reference (9):
  • Y. Z. Zhe Li , Lianwen Jin , Songxuan Lai, “Improving Attention-Based Handwritten Mathematical Expression Recognition with Scale Augmentation and Drop Attention,” Proc. 17th Int. Conf. Front. Handwrit. Recognition, pp. 175-080, 2020.
  • M. Luong, H. Pham, and C. D. Manning, “Effective Approaches to Attention-based Neural Machine Translation,” Proc. 2015 Conf. Empir. Methods Nat. Lang. Process., p. 1412-1421, 2015.
  • J. Zhang et al., “Watch, attend and parse: An end-to-end neural network based approach to handwritten mathematical expression recognition,” Pattern Recognit., vol. 71, pp. 196-206, Nov. 2017, doi:10.1016/j.patcog.2017.06.017.
  • Y. Deng, A. Kanervisto, J. Ling, and A. M. Rush, “Image-to-Markup Generation with Coarseto-Fine Attention,” 34th Int. Conf. Mach. Learn. ICML 2017, vol. 3, pp. 1631-1640, Sep. 2016.
  • T.-N. Truong, C. T. Nguyen, K. M. Phan, and M. Nakagawa, “Improvement of End-to-End Offline Handwritten Mathematical Expression Recognition by Weakly Supervised Learning,” Proc. 17th Int. Conf. Front. Handwrit. Recognit., pp. 181-186, 2020.
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