Skip to main content
Springer Nature Link
Log in

OCR-Free Document Understanding Transformer

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13688))

Included in the following conference series:

Abstract

Understanding document images (e.g., invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document. Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs. Although such OCR-based approaches have shown promising performance, they suffer from 1) high computational costs for using OCR; 2) inflexibility of OCR models on languages or types of documents; 3) OCR error propagation to the subsequent process. To address these issues, in this paper, we introduce a novel OCR-free VDU model named Donut, which stands for Document understanding transformer. As the first step in OCR-free VDU research, we propose a simple architecture (i.e., Transformer) with a pre-training objective (i.e., cross-entropy loss). Donut is conceptually simple yet effective. Through extensive experiments and analyses, we show a simple OCR-free VDU model, Donut, achieves state-of-the-art performances on various VDU tasks in terms of both speed and accuracy. In addition, we offer a synthetic data generator that helps the model pre-training to be flexible in various languages and domains. The code, trained model, and synthetic data are available at https://github.com/clovaai/donut.

T. Hong, M. Yim, J. Park, J. Yim and W. Hwang—This work was done while the authors were at NAVER CLOVA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/clovaai/donut.

  2. 2.

    https://huggingface.co/hyunwoongko/asian-bart-ecjk.

  3. 3.

    https://huggingface.co/datasets/naver-clova-ix/cord-v1.

  4. 4.

    https://rrc.cvc.uab.es/?ch=17.

  5. 5.

    https://clova.ai/ocr.

  6. 6.

    https://rrc.cvc.uab.es/?ch=17 &com=evaluation &task=1.

References

  1. Afzal, M.Z., et al.: Deepdocclassifier: document classification with deep convolutional neural network. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1111–1115 (2015). https://doi.org/10.1109/ICDAR.2015.7333933

  2. 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 (ICCV), pp. 993–1003, October 2021

    Google Scholar 

  3. Baek, J., et al.: What is wrong with scene text recognition model comparisons? Dataset and model analysis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019

    Google Scholar 

  4. Baek, Y., Lee, B., Han, D., Yun, S., Lee, H.: Character region awareness for text detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9357–9366 (2019). https://doi.org/10.1109/CVPR.2019.00959

  5. Brown, T., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901. Curran Associates, Inc. (2020). https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf

  6. Davis, B., Morse, B., Cohen, S., Price, B., Tensmeyer, C.: Deep visual template-free form parsing. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 134–141 (2019). https://doi.org/10.1109/ICDAR.2019.00030

  7. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  8. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171–4186. Association for Computational Linguistics, June 2019. https://doi.org/10.18653/v1/N19-1423. https://aclanthology.org/N19-1423

  9. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3–7 May 2021. OpenReview.net (2021). https://openreview.net/forum?id=YicbFdNTTy

  10. Duong, Q., Hämäläinen, M., Hengchen, S.: An unsupervised method for OCR post-correction and spelling normalisation for Finnish. In: Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), Sweden, Reykjavik, Iceland, 31 May–2 June 2021, pp. 240–248. Linköping University Electronic Press (2021). https://aclanthology.org/2021.nodalida-main.24

  11. Friedl, J.E.F.: Mastering Regular Expressions, 3 edn. O’Reilly, Beijing (2006). https://www.safaribooksonline.com/library/view/mastering-regular-expressions/0596528124/

  12. 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 (2019). https://doi.org/10.1109/ICDAR.2019.00049

  13. Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

    Google Scholar 

  14. Hammami, M., Héroux, P., Adam, S., d’Andecy, V.P.: One-shot field spotting on colored forms using subgraph isomorphism. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 586–590 (2015). https://doi.org/10.1109/ICDAR.2015.7333829

  15. Harley, A.W., Ufkes, A., Derpanis, K.G.: Evaluation of deep convolutional nets for document image classification and retrieval. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 991–995 (2015). https://doi.org/10.1109/ICDAR.2015.7333910

  16. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90

  17. Hong, T., Kim, D., Ji, M., Hwang, W., Nam, D., Park, S.: Bros: a pre-trained language model focusing on text and layout for better key information extraction from documents. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 10, pp. 10767–10775, June 2022. https://doi.org/10.1609/aaai.v36i10.21322. https://ojs.aaai.org/index.php/AAAI/article/view/21322

  18. Huang, W., Qiao, Yu., Tang, X.: Robust scene text detection with convolution neural network induced MSER trees. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 497–511. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_33

    Chapter  Google Scholar 

  19. Huang, Z., et al.: ICDAR 2019 competition on scanned receipt OCR and information extraction. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1516–1520 (2019). https://doi.org/10.1109/ICDAR.2019.00244

  20. Hwang, A., Frey, W.R., McKeown, K.: Towards augmenting lexical resources for slang and African American English. In: Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects, Barcelona, Spain, pp. 160–172. International Committee on Computational Linguistics (ICCL), December 2020. https://aclanthology.org/2020.vardial-1.15

  21. Hwang, W., et al.: Post-OCR parsing: building simple and robust parser via bio tagging. In: Workshop on Document Intelligence at NeurIPS 2019 (2019)

    Google Scholar 

  22. Hwang, W., Lee, H., Yim, J., Kim, G., Seo, M.: Cost-effective end-to-end information extraction for semi-structured document images. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3375–3383. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, November 2021. https://doi.org/10.18653/v1/2021.emnlp-main.271. https://aclanthology.org/2021.emnlp-main.271

  23. Hwang, W., Yim, J., Park, S., Yang, S., Seo, M.: Spatial dependency parsing for semi-structured document information extraction. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 330–343. Association for Computational Linguistics, August 2021. https://doi.org/10.18653/v1/2021.findings-acl.28. https://aclanthology.org/2021.findings-acl.28

  24. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. In: Workshop on Deep Learning, NIPS (2014)

    Google Scholar 

  25. Kang, L., Kumar, J., Ye, P., Li, Y., Doermann, D.S.: Convolutional neural networks for document image classification. In: 2014 22nd International Conference on Pattern Recognition, pp. 3168–3172 (2014)

    Google Scholar 

  26. Karatzas, D., et al.: ICDAR 2015 competition on robust reading. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1156–1160 (2015). https://doi.org/10.1109/ICDAR.2015.7333942

  27. Kim, W., Son, B., Kim, I.: ViLT: vision-and-language transformer without convolution or region supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, 18–24 July 2021, vol. 139, pp. 5583–5594. PMLR (2021). http://proceedings.mlr.press/v139/kim21k.html

  28. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings (2015), http://arxiv.org/abs/1412.6980

  29. Klaiman, S., Lehne, M.: DocReader: bounding-box free training of a document information extraction model. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12821, pp. 451–465. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86549-8_29

    Chapter  Google Scholar 

  30. Lewis, D., Agam, G., Argamon, S., Frieder, O., Grossman, D., Heard, J.: Building a test collection for complex document information processing. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, pp. 665–666. Association for Computing Machinery, New York (2006). https://doi.org/10.1145/1148170.1148307

  31. Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871–7880. Association for Computational Linguistics, July 2020. https://doi.org/10.18653/v1/2020.acl-main.703. https://aclanthology.org/2020.acl-main.703

  32. Li, C., et al.: StructuralLM: structural pre-training for form understanding. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 6309–6318. Association for Computational Linguistics, August 2021. https://doi.org/10.18653/v1/2021.acl-long.493. https://aclanthology.org/2021.acl-long.493

  33. Li, P., et al.: SelfDoc: self-supervised document representation learning. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5648–5656 (2021). https://doi.org/10.1109/CVPR46437.2021.00560

  34. Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: Textboxes: a fast text detector with a single deep neural network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1, February 2017. https://doi.org/10.1609/aaai.v31i1.11196. https://ojs.aaai.org/index.php/AAAI/article/view/11196

  35. Liu, W., Chen, C., Wong, K.Y.K., Su, Z., Han, J.: Star-net: a spatial attention residue network for scene text recognition. In: Richard C. Wilson, E.R.H., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 43.1–43.13. BMVA Press, September 2016. https://doi.org/10.5244/C.30.43

  36. Liu, Y., et al.: Multilingual denoising pre-training for neural machine translation. Trans. Assoc. Comput. Linguist. 8, 726–742 (2020). https://aclanthology.org/2020.tacl-1.47

  37. Liu, Y., Chen, H., Shen, C., He, T., Jin, L., Wang, L.: ABCNet: real-time scene text spotting with adaptive Bezier-curve network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020

    Google Scholar 

  38. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10012–10022, October 2021

    Google Scholar 

  39. Long, S., Yao, C.: Unrealtext: synthesizing realistic scene text images from the unreal world. arXiv preprint arXiv:2003.10608 (2020)

  40. Majumder, B.P., Potti, N., Tata, S., Wendt, J.B., Zhao, Q., Najork, M.: Representation learning for information extraction from form-like documents. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6495–6504. Association for Computational Linguistics, July 2020. https://doi.org/10.18653/v1/2020.acl-main.580. https://www.aclweb.org/anthology/2020.acl-main.580

  41. Majumder, B.P., Potti, N., Tata, S., Wendt, J.B., Zhao, Q., Najork, M.: Representation learning for information extraction from form-like documents. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6495–6504. Association for Computational Linguistics, July 2020. https://doi.org/10.18653/v1/2020.acl-main.580. https://aclanthology.org/2020.acl-main.580

  42. Mathew, M., Karatzas, D., Jawahar, C.: DocVQA: a dataset for VQA on document images. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2200–2209 (2021)

    Google Scholar 

  43. Park, S., et al.: Cord: a consolidated receipt dataset for post-OCR parsing. In: Workshop on Document Intelligence at NeurIPS 2019 (2019)

    Google Scholar 

  44. Peng, D., et al.: SPTS: Single-Point Text Spotting. CoRR abs/2112.07917 (2021). https://arxiv.org/abs/2112.07917

  45. Phan, T.Q., Shivakumara, P., Tian, S., Tan, C.L.: Recognizing text with perspective distortion in natural scenes. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 2013

    Google Scholar 

  46. Powalski, R., Borchmann, Ł, Jurkiewicz, D., Dwojak, T., Pietruszka, M., Pałka, G.: Going full-TILT boogie on document understanding with text-image-layout transformer. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12822, pp. 732–747. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86331-9_47

    Chapter  Google Scholar 

  47. Riba, P., Dutta, A., Goldmann, L., Fornés, A., Ramos, O., Lladó, J.: Table detection in invoice documents by graph neural networks. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 122–127 (2019). https://doi.org/10.1109/ICDAR.2019.00028

  48. Rijhwani, S., Anastasopoulos, A., Neubig, G.: OCR post correction for endangered language texts. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5931–5942. Association for Computational Linguistics, November 2020. https://doi.org/10.18653/v1/2020.emnlp-main.478. https://aclanthology.org/2020.emnlp-main.478

  49. Schaefer, R., Neudecker, C.: A two-step approach for automatic OCR post-correction. In: Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 52–57. International Committee on Computational Linguistics, December 2020. https://aclanthology.org/2020.latechclfl-1.6

  50. 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 Trans. Pattern Anal. Mach. Intell. 39, 2298–2304 (2017)

    Article  Google Scholar 

  51. Shi, B., Wang, X., Lyu, P., Yao, C., Bai, X.: Robust scene text recognition with automatic rectification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4168–4176 (2016). https://doi.org/10.1109/CVPR.2016.452

  52. Taghva, K., Beckley, R., Coombs, J.: The effects of OCR error on the extraction of private information. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 348–357. Springer, Heidelberg (2006). https://doi.org/10.1007/11669487_31

    Chapter  Google Scholar 

  53. Tan, M., Le, Q.: Efficientnetv2: smaller models and faster training. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, 18–24 July 2021, vol. 139, pp. 10096–10106. PMLR (2021). https://proceedings.mlr.press/v139/tan21a.html

  54. Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 56–72. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_4

    Chapter  Google Scholar 

  55. Tito, R., Mathew, M., Jawahar, C.V., Valveny, E., Karatzas, D.: ICDAR 2021 competition on document visual question answering. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12824, pp. 635–649. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86337-1_42

    Chapter  Google Scholar 

  56. Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

  57. Wang, J., Hu, X.: Gated recurrent convolution neural network for OCR. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). https://proceedings.neurips.cc/paper/2017/file/c24cd76e1ce41366a4bbe8a49b02a028-Paper.pdf

  58. Wang, S., Li, B., Khabsa, M., Fang, H., Ma, H.: Linformer: self-attention with linear complexity. arXiv preprint arXiv:2006.04768 (2020)

  59. Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)

    Article  Google Scholar 

  60. Xu, Y., et al.: LayoutLMv2: multi-modal pre-training for visually-rich document understanding. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 2579–2591. Association for Computational Linguistics, August 2021. https://doi.org/10.18653/v1/2021.acl-long.201. https://aclanthology.org/2021.acl-long.201

  61. 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, KDD 2020, pp. 1192–1200. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3394486.3403172

  62. Xu, Y., et al.: Layoutxlm: multimodal pre-training for multilingual visually-rich document understanding. arXiv preprint arXiv:2104.08836 (2021)

  63. Yim, M., Kim, Y., Cho, H.-C., Park, S.: SynthTIGER: synthetic text image GEneratoR towards better text recognition models. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12824, pp. 109–124. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86337-1_8

    Chapter  Google Scholar 

  64. Zhang, K., Shasha, D.: Simple fast algorithms for the editing distance between trees and related problems. SIAM J. Comput. 18, 1245–1262 (1989). https://doi.org/10.1137/0218082

  65. Zhang, Z., Zhang, C., Shen, W., Yao, C., Liu, W., Bai, X.: Multi-oriented text detection with fully convolutional networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4159–4167 (2016). https://doi.org/10.1109/CVPR.2016.451

  66. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 564–580. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58589-1_34

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NAVER CLOVA, Seongnam-si, South Korea

    Geewook Kim, JeongYeon Nam & Seunghyun Park

  2. NAVER Search, Seongnam-si, South Korea

    Moonbin Yim

  3. NAVER AI Lab, Seongnam-si, South Korea

    Sangdoo Yun & Dongyoon Han

  4. Upstage, Yongin-si, South Korea

    Teakgyu Hong

  5. Tmax, Seongnam-si, South Korea

    Jinyoung Park

  6. Google, Seoul, South Korea

    Jinyeong Yim

  7. LBox, Seoul, South Korea

    Wonseok Hwang

Authors
  1. Geewook Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Teakgyu Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Moonbin Yim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. JeongYeon Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jinyoung Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinyeong Yim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wonseok Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sangdoo Yun
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dongyoon Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Seunghyun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Geewook Kim .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 2739 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kim, G. et al. (2022). OCR-Free Document Understanding Transformer. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13688. Springer, Cham. https://doi.org/10.1007/978-3-031-19815-1_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19815-1_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19814-4

  • Online ISBN: 978-3-031-19815-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics