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U-Net Based Architectures for Document Text Detection and Binarization

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

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Abstract

With the increasing popularity of document analysis and recognition systems, text detection (TD) and text binarization (TB) in document images remain challenging tasks. In the paper, we introduced a two-step architecture for the TD task. Firstly, a U-net based model is used to get a text mask in terms of word-level bounding boxes. Secondly, we approximate the mask of the bounding boxes with rectangles using a classic computer vision method. The model achieves state-of-the-art result on document images and outperforms other popular approaches. Moreover, we introduce the Hybrid U-net architecture, which helps to solve the TB and TD problems at the same time. The model demonstrates high results on both problems. The shared convolution encoder allows to reduce the number of parameters and consumed memory compared to separate models without reducing the model performance.

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References

  1. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25, 120–125 (2000)

    Google Scholar 

  2. Busta, M., Neumann, L., Matas, J.: Fastext: efficient unconstrained scene text detector. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1206–1214 (2015)

    Google Scholar 

  3. Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2963–2970. IEEE (2010)

    Google Scholar 

  4. Freeman, H., Shapira, R.: Determining the minimum-area encasing rectangle for an arbitrary closed curve. Commun. ACM 18(7), 409–413 (1975)

    Article  MathSciNet  Google Scholar 

  5. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  6. 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. IEEE (2015)

    Google Scholar 

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

    Google Scholar 

  8. Heijmans, H.J.: Morphological Image Operators, vol. 4. Academic Press, Boston (1994)

    MATH  Google Scholar 

  9. Huang, J., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7310–7311 (2017)

    Google Scholar 

  10. Huang, W., Qiao, Y., 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 

  11. Huang, W., Lin, Z., Yang, J., Wang, J.: Text localization in natural images using stroke feature transform and text covariance descriptors. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1241–1248 (2013)

    Google Scholar 

  12. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Reading text in the wild with convolutional neural networks. Int. J. Comput. Vis. 116(1), 1–20 (2016)

    Article  MathSciNet  Google Scholar 

  13. Jaume, G., Ekenel, H.K., Thiran, J.P.: FUNSD: a dataset for form understanding in noisy scanned documents. arXiv preprint arXiv:1905.13538 (2019)

  14. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  15. 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 

  16. Neubeck, A., Van Gool, L.: Efficient non-maximum suppression. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 3, pp. 850–855. IEEE (2006)

    Google Scholar 

  17. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  18. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  19. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  20. Smith, R.: An overview of the tesseract OCR engine. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 2, pp. 629–633. IEEE (2007)

    Google Scholar 

  21. Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_28

    Chapter  Google Scholar 

  22. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  23. Tian, S., Pan, Y., Huang, C., Lu, S., Yu, K., Lim Tan, C.: Text flow: a unified text detection system in natural scene images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4651–4659 (2015)

    Google Scholar 

  24. 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 

  25. Yin, X.C., Yin, X., Huang, K., Hao, H.W.: Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 970–983 (2013)

    Google Scholar 

  26. Zhou, X., et al.: EAST: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5551–5560 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Dolgoprudny, Russia

    Filipp Nikitin, Vladimir Dokholyan, Ilia Zharikov & Vadim Strijov

Authors
  1. Filipp Nikitin
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  2. Vladimir Dokholyan
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  3. Ilia Zharikov
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  4. Vadim Strijov
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Corresponding author

Correspondence to Filipp Nikitin .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Nikitin, F., Dokholyan, V., Zharikov, I., Strijov, V. (2019). U-Net Based Architectures for Document Text Detection and Binarization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-33723-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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