Abstract
Printed mathematical expression recognition is to transform printed mathematical formula image into LaTeX sequence. Recently, many methods based on deep learning have been proposed to solve this task. However, the positional relationship between mathematical symbols is often ignored or represented insufficient, leading to the loss of structural features of mathematical formulas. To overcome this challenge, we propose a position-aware encoder-decoder model for printed mathematical expression recognition. We design a two-dimensional position encoding algorithm based on sin/cos function to capture positional relationship between mathematical symbols. Meanwhile, we adopt a more advanced image feature extraction network. In decoder component, we use Bi-GRU as the translator, and add attention mechanism to make decoder focus on the important local information. We conduct experiments on the public dataset IM2LaTeX-100K, and the results show that our proposed approach is more excellent than the majority of advanced methods.
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References
Anderson, R.H.: Syntax-directed recognition of hand-printed two-dimensional mathematics. In: Symposium on Interactive Systems for Experimental Applied Mathematics: Proceedings of the Association for Computing Machinery Inc., Symposium, pp. 436–459 (1967)
Bian, X., Qin, B., Xin, X., Li, J., Su, X., Wang, Y.: Handwritten mathematical expression recognition via attention aggregation based bi-directional mutual learning. arXiv preprint arXiv:2112.03603 (2021)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Deng, Y., Kanervisto, A., Rush, A.M.: What you get is what you see: a visual markup decompiler (2016)
Deng, Y., Kanervisto, A., Ling, J., Rush, A.M.: Image-to-markup generation with coarse-to-fine attention (2017)
Graves, A.: Sequence transduction with recurrent neural networks. arXiv preprint arXiv:1211.3711 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. IEEE (2016)
Huang, G., Liu, Z., Laurens, V., Weinberger, K.Q.: Densely connected convolutional networks. IEEE Computer Society (2016)
Huang, Z., et al.: Question difficulty prediction for reading problems in standard tests. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128–3137 (2015)
Li, B., Yuan, Y., Liang, D., Liu, X., Ji, Z., Bai, J., Liu, W., Bai, X.: When counting meets hmer: Counting-aware network for handwritten mathematical expression recognition. In: European Conference on Computer Vision, pp. 197–214. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19815-1_12
Li, Z., Jin, L., Lai, S., Zhu, Y.: Improving attention-based handwritten mathematical expression recognition with scale augmentation and drop attention. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 175–180. IEEE (2020)
Liu, Q., Huang, Z., Huang, Z., Liu, C., Chen, E., Su, Y., Hu, G.: Finding similar exercises in online education systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1821–1830 (2018)
Liu, Q., et al.: Fuzzy cognitive diagnosis for modelling examinee performance. ACM Trans. Intell. Syst. Technol. (TIST) 9(4), 1–26 (2018)
Lu, J., Yang, J., Batra, D., Parikh, D.: Neural baby talk. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7219–7228 (2018)
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
Pang, N., Yang, C., Zhu, X., Li, J., Yin, X.C.: Global context-based network with transformer for image2latex. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 4650–4656. IEEE (2021)
Peng, S., Gao, L., Yuan, K., Tang, Z.: Image to latex with graph neural network for mathematical formula recognition. In: International Conference on Document Analysis and Recognition, pp. 648–663. Springer (2021)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Suzuki, M., Tamari, F., Kanahori, T.: Infty – an integrated OCR system for (2003)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Vaswani, A., et al.: Attention is all you need. Advances in neural information processing systems 30 (2017)
Wang, J., Sun, Y., Wang, S.: Image to latex with densenet encoder and joint attention. Procedia Comput. Sci. 147, 374–380 (2019)
Wu, J.W., Yin, F., Zhang, Y.M., Zhang, X.Y., Liu, C.L.: Handwritten mathematical expression recognition via paired adversarial learning. Int. J. Comput. Vis., 1–16 (2020)
Wu, J.W., Yin, F., Zhang, Y.M., Zhang, X.Y., Liu, C.L.: Graph-to-graph: towards accurate and interpretable online handwritten mathematical expression recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2925–2933 (2021)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Yan, Z., Zhang, X., Gao, L., Yuan, K., Tang, Z.: Convmath: a convolutional sequence network for mathematical expression recognition. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 4566–4572. IEEE (2021)
Zhang, J., Du, J., Dai, L.: Track, attend, and parse (tap): an end-to-end framework for online handwritten mathematical expression recognition. IEEE Trans. Multimedia 21(1), 221–233 (2018)
Zhang, W., Bai, Z., Zhu, Y.: An improved approach based on CNN-RNNs for mathematical expression recognition. In: Proceedings of the 2019 4th International Conference on Multimedia Systems and Signal Processing, pp. 57–61 (2019)
Acknowledgements
This work is being supported by the National Natural Science Foundation of China under the Grant No. U2003208 and No. 62172451, and supported by Open Research Projects of Zhejiang Lab under the Grant No. 2022KG0AB01.
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Long, J., Hong, Q., Yang, L. (2023). An Encoder-Decoder Method with Position-Aware for Printed Mathematical Expression Recognition. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14187. Springer, Cham. https://doi.org/10.1007/978-3-031-41676-7_10
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DOI: https://doi.org/10.1007/978-3-031-41676-7_10
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