Author:
Gbada Hamza,Kalti Karim,Mahjoub Mohamed Ali
Publisher
Springer Science and Business Media LLC
Reference84 articles.
1. Park, S., Shin, S., Lee, B., Lee, J., Surh, J., Seo, M., Lee, H.: CORD: a consolidated receipt dataset for post-OCR parsing. In: Workshop on Document Intelligence at NeurIPS 2019 (2019)
2. Jaume, G., Ekenel, H.K., Thiran, J.-P.: FUNSD: a dataset for form understanding in noisy scanned documents. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 2, pp. 1–6. IEEE (2019)
3. Huang, Z., Chen, K., He, J., Bai, X., Karatzas, D., Lu, S., Jawahar, C.: Icdar2019 competition on scanned receipt OCR and information extraction. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1516–1520. IEEE (2019)
4. Sun, H., Kuang, Z., Yue, X., Lin, C., Zhang, W.: Spatial dual-modality graph reasoning for key information extraction. arXiv preprint arXiv:2103.14470 (2021)
5. Li, M., Xu, Y., Cui, L., Huang, S., Wei, F., Li, Z., Zhou, M.: DocBank: a benchmark dataset for document layout analysis. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 949–960. International Committee on Computational Linguistics, Barcelona (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.82. https://aclanthology.org/2020.coling-main.82