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A Hybrid Approach to Document Layout Analysis for Heterogeneous Document Images

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Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14191))

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Abstract

We present a new hybrid document layout analysis approach to simultaneously detecting graphical page objects, group text-lines into text regions according to reading order, and recognize the logical roles of text regions from heterogeneous document images. For graphical page object detection, we leverage a state-of-the-art Transformer-based object detection model, namely DINO, as a new graphical page object detector to detect tables, figures, and (displayed) formulas in a top-down manner. Furthermore, we introduce a new bottom-up text region detection model to group text-lines located outside graphical page objects into text regions according to reading order and recognize the logical role of each text region by using both visual and textual features. Experimental results show that our bottom-up text region detection model achieves higher localization and logical role classification accuracy than previous top-down methods. Moreover, in addition to the locations of text regions, our approach can also output the reading order of text-lines in each text region directly. The state-of-the-art results obtained on DocLayNet and PubLayNet demonstrate the effectiveness of our approach.

J. Wang, H. Sun, K. Hu and E. Zhang—This work was done when Jiawei Wang, Haiqing Sun, Kai Hu and Erhan Zhang were interns in MMI Group, Microsoft Research Asia, Beijing, China.

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References

  1. Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)

  2. Bi, H., et al.: Srrv: A novel document object detector based on spatial-related relation and vision. IEEE Transactions on Multimedia (2022)

    Google Scholar 

  3. Binmakhashen, G.M., Mahmoud, S.A.: Document layout analysis: a comprehensive survey. ACM Comput. Surv. (CSUR) 52(6), 1–36 (2019)

    Article  Google Scholar 

  4. Biswas, S., Banerjee, A., Lladós, J., Pal, U.: Docsegtr: an instance-level end-to-end document image segmentation transformer. arXiv preprint arXiv:2201.11438 (2022)

  5. Cai, Z., Vasconcelos, N.: Cascade r-cnn: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1483–1498 (2019)

    Article  Google Scholar 

  6. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proceedings of the European Conference on Computer Vision, pp. 213–229 (2020)

    Google Scholar 

  7. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proceedings of the European Conference on Computer Vision, pp. 213–229 (2020)

    Google Scholar 

  8. Dai, X., et al.: Dynamic head: Unifying object detection heads with attentions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7373–7382 (2021)

    Google Scholar 

  9. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171–4186 (2019)

    Google Scholar 

  10. Doermann, D., Tombre, K. (eds.): Handbook of Document Image Processing and Recognition. Springer, London (2014). https://doi.org/10.1007/978-0-85729-859-1

  11. Girshick, R.: Fast r-cnn. In: Proceedings of the International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  12. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  13. Gu, J., et al.: Unified pretraining framework for document understanding. arXiv preprint arXiv:2204.10939 (2022)

  14. He, D., Cohen, S., Price, B., Kifer, D., Giles, C.L.: Multi-scale multi-task fcn for semantic page segmentation and table detection. In: Proceedings of the International Conference on Document Analysis and Recognition. vol. 1, pp. 254–261 (2017)

    Google Scholar 

  15. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-CNN. In: Proceedings of the International Conference on Computer Visio, pp. 2961–2969 (2017)

    Google Scholar 

  16. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  17. Huang, Y., Lv, T., Cui, L., Lu, Y., Wei, F.: Layoutlmv3: Pre-training for document ai with unified text and image masking. In: Proceedings of the ACM International Conference on Multimedia, pp. 4083–4091 (2022)

    Google Scholar 

  18. Jocher, G., et al.: ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations (Apr 2021)

    Google Scholar 

  19. Li, F., Zhang, H., Liu, S., Guo, J., Ni, L.M., Zhang, L.: Dn-detr: Accelerate detr training by introducing query denoising. arXiv preprint arXiv:2203.01305 (2022)

  20. Li, J., Xu, Y., Lv, T., Cui, L., Zhang, C., Wei, F.: Dit: Self-supervised pre-training for document image transformer. In: Proceedings of the ACM International Conference on Multimedia. pp. 3530–3539 (2022)

    Google Scholar 

  21. Li, X.H., Yin, F., Liu, C.L.: Page object detection from pdf document images by deep structured prediction and supervised clustering. In: Proceedings of the International Conference on Pattern Recognition, pp. 3627–3632 (2018)

    Google Scholar 

  22. Li, X.H., Yin, F., Liu, C.L.: Page segmentation using convolutional neural network and graphical model. In: Proceedings of the International Workshop on Document Analysis Systems, pp. 231–245 (2020)

    Google Scholar 

  23. Li, X.H., et al.: Instance aware document image segmentation using label pyramid networks and deep watershed transformation. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 514–519 (2019)

    Google Scholar 

  24. Li, Y., Zou, Y., Ma, J.: Deeplayout: A semantic segmentation approach to page layout analysis. In: Proceedings of the International Conference on Intelligent Computing Methodologies, pp. 266–277 (2018)

    Google Scholar 

  25. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  26. Liu, S., et al.: Dab-detr: Dynamic anchor boxes are better queries for detr. arXiv preprint arXiv:2201.12329 (2022)

  27. Liu, S., Wang, R., Raptis, M., Fujii, Y.: Unified line and paragraph detection by graph convolutional networks. In: Proceedings of the International Workshop on Document Analysis Systems, pp. 33–47 (2022)

    Google Scholar 

  28. Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10012–10022 (2021)

    Google Scholar 

  29. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  30. Long, S., Qin, S., Panteleev, D., Bissacco, A., Fujii, Y., Raptis, M.: Towards end-to-end unified scene text detection and layout analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1049–1059 (2022)

    Google Scholar 

  31. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

  32. Luo, S., Ding, Y., Long, S., Han, S.C., Poon, J.: Doc-gcn: Heterogeneous graph convolutional networks for document layout analysis. arXiv preprint arXiv:2208.10970 (2022)

  33. Minouei, M., Soheili, M.R., Stricker, D.: Document layout analysis with an enhanced object detector. In: Proceedings of the International Conference on Pattern Recognition and Image Analysis, pp. 1–5 (2021)

    Google Scholar 

  34. Naik, S., Hashmi, K.A., Pagani, A., Liwicki, M., Stricker, D., Afzal, M.Z.: Investigating attention mechanism for page object detection in document images. Appl. Sci. 12(15), 7486 (2022)

    Article  Google Scholar 

  35. Oliveira, D.A.B., Viana, M.P.: Fast cnn-based document layout analysis. In: Proceedings of the International Conference on Computer Vision Workshops, pp. 1173–1180 (2017)

    Google Scholar 

  36. Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.W.: Doclaynet: A large human-annotated dataset for document-layout analysis. arXiv preprint arXiv:2206.01062 (2022)

  37. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  38. Saha, R., Mondal, A., Jawahar, C.: Graphical object detection in document images. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 51–58 (2019)

    Google Scholar 

  39. Sang, Y., Zeng, Y., Liu, R., Yang, F., Yao, Z., Pan, Y.: Exploiting spatial attention and contextual information for document image segmentation. In: Proceedings of the Advances in Knowledge Discovery and Data Mining, pp. 261–274 (2022)

    Google Scholar 

  40. Shi, C., Xu, C., Bi, H., Cheng, Y., Li, Y., Zhang, H.: Lateral feature enhancement network for page object detection. IEEE Trans. Instrum. Meas. 71, 1–10 (2022)

    Google Scholar 

  41. Sun, P., et al.: Sparse r-cnn: End-to-end object detection with learnable proposals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 14454–14463 (2021)

    Google Scholar 

  42. Vo, N.D., Nguyen, K., Nguyen, T.V., Nguyen, K.: Ensemble of deep object detectors for page object detection. In: Proceedings of the International Conference on Ubiquitous Information Management and Communicatio, pp. 1–6 (2018)

    Google Scholar 

  43. Wang, R., Fujii, Y., Popat, A.C.: Post-ocr paragraph recognition by graph convolutional networks. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 493–502 (2022)

    Google Scholar 

  44. Wang, X., Zhang, R., Kong, T., Li, L., Shen, C.: Solov2: Dynamic and fast instance segmentation. In: Proceedings of the Advances in Neural information processing systems. vol. 33, pp. 17721–17732 (2020)

    Google Scholar 

  45. Xue, C., Huang, J., Zhang, W., Lu, S., Wang, C., Bai, S.: Contextual text block detection towards scene text understanding. In: Proceedings of the European Conference on Computer Vision, pp. 374–391 (2022)

    Google Scholar 

  46. Yang, H., Hsu, W.: Transformer-based approach for document layout understanding. In: Proceedings of the International Conference on Image Processing, pp. 4043–4047 (2022)

    Google Scholar 

  47. Yang, X., Yumer, E., Asente, P., Kraley, M., Kifer, D., Lee Giles, C.: Learning to extract semantic structure from documents using multimodal fully convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5315–5324 (2017)

    Google Scholar 

  48. Yi, X., Gao, L., Liao, Y., Zhang, X., Liu, R., Jiang, Z.: Cnn based page object detection in document images. In: Proceedings of the International Conference on Document Analysis and Recognition. vol. 1, pp. 230–235 (2017)

    Google Scholar 

  49. Zhang, H., et al.: Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 (2022)

  50. Zhang, J., Elhoseiny, M., Cohen, S., Chang, W., Elgammal, A.: Relationship proposal networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5678–5686 (2017)

    Google Scholar 

  51. Zhang, P., et al.: Vsr: a unified framework for document layout analysis combining vision, semantics and relations. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 115–130 (2021)

    Google Scholar 

  52. Zhang, Y., Bo, Z., Wang, R., Cao, J., Li, C., Bao, Z.: Entity relation extraction as dependency parsing in visually rich documents. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 2759–2768 (2021)

    Google Scholar 

  53. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 1015–1022 (2019)

    Google Scholar 

  54. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: Proceedings of the International Conference on Learning Representations (2021)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Zhuoyao Zhong, Jiawei Wang, Haiqing Sun, Kai Hu, Erhan Zhang, Lei Sun & Qiang Huo

  2. Department of EEIS, University of Science and Technology of China, Hefei, China

    Jiawei Wang & Kai Hu

  3. School of Software and Microelectronics, Peking University, Beijing, China

    Haiqing Sun & Erhan Zhang

Authors
  1. Zhuoyao Zhong
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  2. Jiawei Wang
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  4. Kai Hu
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  6. Lei Sun
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  7. Qiang Huo
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Correspondence to Zhuoyao Zhong .

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Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. Adobe, College Park, MN, USA

    Rajiv Jain

  3. Osaka Metropolitan University, Osaka, Japan

    Koichi Kise

  4. Rochester Institute of Technology, Rochester, NY, USA

    Richard Zanibbi

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Zhong, Z. et al. (2023). A Hybrid Approach to Document Layout Analysis for Heterogeneous Document Images. 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 14191. Springer, Cham. https://doi.org/10.1007/978-3-031-41734-4_12

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  • DOI: https://doi.org/10.1007/978-3-031-41734-4_12

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