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Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI.
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis and machine intelligence 39
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Guo, H., Qin, X., Liu, J., Han, J., Liu, J., Ding, E.: Eaten: Entity-aware attention for single shot visual text extraction. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 254–259. IEEE (2019)
2019
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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)
2019
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Jaume, G., Ekenel, H.K., Thiran, J.P.: Funsd: A dataset for form understanding in noisy scanned documents. In: ICDARW. vol. 2, pp. 1–6 (2019)
2019
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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)
2019
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Zhang, C., Liang, B., Huang, Z., En, M., Han, J., Ding, E., Ding, X.: Look more than once: An accurate detector for text of arbitrary shapes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10552–10561 (2019)
2019
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Liao, M., Wan, Z., Yao, C., Chen, K., Bai, X.: Real-time scene text detection with differentiable binarization. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 11474–11481 (2020)
2020
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Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., Zhou, M.: Layoutlm: Pre-training of text and layout for document image understanding. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 1192–1200 (2020)
2020
Cited alongside, same era.
Yu, D., Li, X., Zhang, C., Liu, T., Han, J., Liu, J., Ding, E.: Towards accurate scene text recognition with semantic reasoning networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12113–12122 (2020)
2020
Cited alongside, same era.
Zhang, P., Xu, Y., Cheng, Z., Pu, S., Lu, J., Qiao, L., Niu, Y., Wu, F.: Trie: end-to-end text reading and information extraction for document understanding. In: Proceedings of the 28th ACM International Conference on Multimedia. pp. 1413–1422 (2020)
2020
Cited alongside, same era.
Appalaraju, S., Jasani, B., Kota, B.U., Xie, Y., Manmatha, R.: Docformer: End-to-end transformer for document understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 993–1003 (2021)
Yu, W., Lu, N., Qi, X., Gong, P., Xiao, R.: Pick: processing key information extraction from documents using improved graph learning-convolutional networks. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 4363–4370. IEEE (2021)
2021
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Cao, H., Li, X., Ma, J., Jiang, D., Guo, A., Hu, Y., Liu, H., Liu, Y., Ren, B.: Query-driven generative network for document information extraction in the wild. In: Proceedings of the 30th ACM International Conference on Multimedia (2022)
2022
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Cao, H., Ma, J., Guo, A., Hu, Y., Liu, H., Jiang, D., Liu, Y., Ren, B.: Gmn: Generative multi-modal network for practical document information extraction. In: North American Chapter of the Association for Computational Linguistics (2022)
2022
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Kim, G., Hong, T., Yim, M., Nam, J., Park, J., Yim, J., Hwang, W., Yun, S., Han, D., Park, S.: Ocr-free document understanding transformer. In: European Conference on Computer Vision (ECCV) (2022)
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2021
Cited alongside, same era.
Fang, S., Xie, H., Wang, Y., Mao, Z., Zhang, Y.: Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7098–7107 (2021)
2021
Cited alongside, same era.
Li, Y., Qian, Y., Yu, Y., Qin, X., Zhang, C., Liu, Y., Yao, K., Han, J., Liu, J., Ding, E.: Structext: Structured text understanding with multi-modal transformers. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 1912–1920 (2021)
2021
Cited alongside, same era.
Wang, J., Liu, C., Jin, L., Tang, G., Zhang, J., Zhang, S., Wang, Q., Wu, Y., Cai, M.: Towards robust visual information extraction in real world: new dataset and novel solution. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 2738–2745 (2021)
2021
Cited alongside, same era.
2022
Later among the works it cites.
Xu, Y., Lv, T., Cui, L., Wang, G., Lu, Y., Florencio, D., Zhang, C., Wei, F.: Xfund: A benchmark dataset for multilingual visually rich form understanding. In: Findings of the Association for Computational Linguistics: ACL 2022. pp. 3214–3224 (2022)
2022
Later among the works it cites.
Zhang, Z., Ma, J., Du, J., Wang, L., Zhang, J.: Multimodal pre-training based on graph attention network for document understanding. IEEE Transactions on Multimedia (2022)
2022
Later among the works it cites.
Kuang, J., Hua, W., Liang, D., Yang, M., Jiang, D., Ren, B., Zhou, Y., Bai, X.: Visual information extraction in the wild: Practical dataset and end-to-end solution. In: ICDAR (2023)
2023
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