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Text Line Segmentation: A FCN Based Approach

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Computer Vision and Image Processing (CVIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1377))

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Abstract

Text line segmentation is a prerequisite for most of the document processing systems. However, for handwritten/warped documents, it is not straightforward to segment the text lines. This work proposes a learning-based text line segmentation method from document images. This work can tackle complex layouts present in a camera captured or handwritten document images along with printed flat-bed scanned English documents. The method also works for Alphasyllabrary scripts like Bangla. Segmentation of Bangla handwritten text is quite challenging because of its unique characteristics. The proposed approach of line segmentation relies on fully convolutional networks (FCNs). To improve the performance of the method, we introduce a post-processing step. The model is trained and tested on our dataset along with the cBAD dataset. We develop the model in such a way that it can be trained and tested in a machine that has limited access to highly computational accessories like GPU. The results of our experiments are encouraging.

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References

  1. Banumathi, K.L., Chandra, A.P.J.: Line and word segmentation of Kannada handwritten text documents using projection profile technique. In: 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), pp. 196–201 (2016)

    Google Scholar 

  2. Barakat, B., Droby, A., Kassis, M., El-Sana, J.: Text line segmentation for challenging handwritten document images using fully convolutional network. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 374–379 (2018)

    Google Scholar 

  3. Bukhari, S.S., Shafait, F., Breuel, T.M.: Script-independent handwritten textlines segmentation using active contours. In: 2009 10th International Conference on Document Analysis and Recognition, pp. 446–450 (2009)

    Google Scholar 

  4. Bukhari, S.S., Shafait, F., Breuel, T.M.: Border noise removal of camera-captured document images using page frame detection. In: Iwamura, M., Shafait, F. (eds.) CBDAR 2011. LNCS, vol. 7139, pp. 126–137. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29364-1_10

    Chapter  Google Scholar 

  5. Calderon, G., Angel, M., Hernandez, G., Arnulfo, R., Ledeneva, Y.: Unsupervised multi-language handwritten text line segmentation. J. Intell. Fuzzy Syst 34, 2901–2911 (2018)

    Article  Google Scholar 

  6. Chaudhari, S., Gulati, R.: Segmentation problems in handwritten Gujarati text. Int. J. Eng. Res. Technol. (IJERT) 3, 1937–1942 (2014)

    Google Scholar 

  7. Chavan, V., Mehrotra, K.: Text line segmentation of multilingual handwritten documents using fourier approximation. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1–6 (2017)

    Google Scholar 

  8. Dutta, A., Garai, A., Biswas, S.: Segmentation of meaningful text-regions from camera captured document images. In: 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), pp. 1–4 (2018). https://doi.org/10.1109/EAIT.2018.8470403

  9. Farahmand, A., Sarrafzadeh, A., Shanbehzadeh, J.: Document image noises and removal methods, vol. 1, pp. 436–440 (2013)

    Google Scholar 

  10. Garai, A., Biswas, S., Mandal, S., Chaudhuri, B.B.: Automatic dewarping of camera captured born-digital bangla document images. In: 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), pp. 1–6 (2017). https://doi.org/10.1109/ICAPR.2017.8593157

  11. Garai, A., Biswas, S.: Dewarping of single-folded camera captured bangla document images. In: Das, A.K., Nayak, J., Naik, B., Pati, S.K., Pelusi, D. (eds.) Computational Intelligence in Pattern Recognition. AISC, vol. 999, pp. 647–656. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9042-5_55

    Chapter  Google Scholar 

  12. Garai, A., Biswas, S., Mandal, S.: A theoretical justification of warping generation for dewarping using CNN. Pattern Recogn. 109, 107621 (2021). https://doi.org/10.1016/j.patcog.2020.107621

    Article  Google Scholar 

  13. Garai, A., Biswas, S., Mandal, S., Chaudhuri, B.B.: Automatic rectification of warped bangla document images. IET Image Process. 14(9), 74–83 (2020)

    Article  Google Scholar 

  14. Garg, N.K., Kaur, L., Jindal, M.K.: A new method for line segmentation of handwritten Hindi text. In: 2010 Seventh International Conference on Information Technology: New Generations, pp. 392–397 (2010)

    Google Scholar 

  15. Gatos, B., Stamatopoulos, N., Louloudis, G.: ICDAR 2009 handwriting segmentation contest. In: 2009 10th International Conference on Document Analysis and Recognition, pp. 1393–1397 (2009)

    Google Scholar 

  16. Kumar, M.R., Pradeep, R., Kumar, B.S.P., Babu, P.: Article: a simple text-line segmentation method for handwritten documents. In: IJCA Proceedings on National Conference on Advanced Computing and Communications 2012 NCACC(1), pp. 46–61 (2012). Full text available

    Google Scholar 

  17. Li, X., Yin, F., Xue, T., Liu, L., Ogier, J., Liu, C.: Instance aware document image segmentation using label pyramid networks and deep watershed transformation. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 514–519 (2019). https://doi.org/10.1109/ICDAR.2019.00088

  18. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015). https://doi.org/10.1109/CVPR.2015.7298965

  19. Diem, M, Kleber, F., Fiel, S., Gruning, T., Gatos, B.: CBAD: ICDAR 2017 competition on baseline detection. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 1355–1360 (2017)

    Google Scholar 

  20. Mullick, K., Banerjee, S., Bhattacharya, U.: An efficient line segmentation approach for handwritten bangla document image. In: 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), pp. 1–6 (2015)

    Google Scholar 

  21. Pratikakis, I., Zagoris, K., Karagiannis, X., Tsochatzidis, L., Mondal, T., Marthot-Santaniello, I.: ICDAR 2019 competition on document image binarization (dibco 2019). In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1547–1556 (2019)

    Google Scholar 

  22. Renton, G., Chatelain, C., Adam, S., Kermorvant, C., Paquet, T.: Handwritten text line segmentation using fully convolutional network. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 05, pp. 5–9 (2017)

    Google Scholar 

  23. Renton, G., Soullard, Y., Chatelain, C., Adam, S., Kermorvant, C., Paquet, T.: Fully convolutional network with dilated convolutions for handwritten text line segmentation. Int. J. Doc. Anal. Recogn. (IJDAR) 21(3), 177–186 (2018)

    Article  Google Scholar 

  24. Roy, P., Dutta, S., Dey, N., Dey, G., Chakraborty, S., Ray, R.: Adaptive thresholding: a comparative study. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 1182–1186 (2014)

    Google Scholar 

  25. Shafait, F., Breuel, T.M.: The effect of border noise on the performance of projection-based page segmentation methods. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 846–851 (2011)

    Article  Google Scholar 

  26. Shobha Rani, N., Vasudev, T.: An efficient technique for detection and removal of lines with text stroke crossings in document images. In: Guru, D.S., Vasudev, T., Chethan, H.K., Sharath Kumar, Y.H. (eds.) Proceedings of International Conference on Cognition and Recognition. LNNS, vol. 14, pp. 83–97. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5146-3_9

    Chapter  Google Scholar 

  27. Stamatopoulos, N., Gatos, B., Louloudis, G., Pal, U., Alaei, A.: ICDAR 2013 handwriting segmentation contest. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1402–1406 (2013). https://doi.org/10.1109/ICDAR.2013.283

  28. Vo, Q.N., Lee, G.: Dense prediction for text line segmentation in handwritten document images. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3264–3268 (2016). https://doi.org/10.1109/ICIP.2016.7532963

  29. Xiaojun, D., Wumo, P., Tien, D.B.: Text line segmentation in handwritten documents using mumford shah model. Pattern Recogn. 42(12), 3136–3145 (2009). https://doi.org/10.1016/j.patcog.2008.12.021, http://www.sciencedirect.com/science/article/pii/S0031320308005360, new Frontiers in Handwriting Recognition

  30. Zhang, X., Tan, C.L.: Text line segmentation for handwritten documents using constrained seam carving. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 98–103 (2014)

    Google Scholar 

  31. Zhao, J., Shi, C., Jia, F., Wang, Y., Xiao, B.: An effective binarization method for disturbed camera-captured document images. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 339–344 (2018)

    Google Scholar 

  32. Zirari, F., Ennaji, A., Nicolas, S., Mammass, D.: A document image segmentation system using analysis of connected components. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 753–757 (2013)

    Google Scholar 

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Correspondence to Arpan Garai .

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Minj, A., Garai, A., Mandal, S. (2021). Text Line Segmentation: A FCN Based Approach. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_26

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  • DOI: https://doi.org/10.1007/978-981-16-1092-9_26

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