Abstract
In this work, we propose an end-to-end language agnostic multi-task learning based U-Net framework for performing text block segmentation and baseline detection in document images. We leverage the performance of U-Net by augmenting attention layers between the contracting and expansive path via skip connections. The generalization ability of the model is validated on handwritten images as well. We perform exhaustive experiments on ICPR2020 challenge dataset and obtain a test accuracy of 96.09% and 99.44% for simple track baseline detection and text block segmentation respectively, 97.47% and 98.51% complex track baseline and text block segmentation respectively. The source code is made publicly available at https://github.com/divyanshjoshi/Attention-U-Net-Newspaper-Text-Block-Segmentation.
A. Bansal and P. Mukherjee—The authors have contributed equally.
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References
Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th \(\{\)USENIX\(\}\) Symposium on Operating Systems Design and Implementation (\(\{\)OSDI\(\}\) 2016), pp. 265–283 (2016)
Alhéritière, H., Cloppet, F., Kurtz, C., Ogier, J.M., Vincent, N.: A document straight line based segmentation for complex layout extraction. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1126–1131. IEEE (2017)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Bansal, A., Chaudhury, S., Roy, S.D., Srivastava, J.: Newspaper article extraction using hierarchical fixed point model. In: 2014 11th IAPR International Workshop on Document Analysis Systems, pp. 257–261. IEEE (2014)
Beretta, R., Laura, L.: Performance evaluation of algorithms for newspaper article identification. In: 2011 International Conference on Document Analysis and Recognition, pp. 394–398. IEEE (2011)
Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
Clausner, C., Antonacopoulos, A., Derrick, T., Pletschacher, S.: ICDAR 2019 competition on recognition of early Indian printed documents-REID 2019. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1527–1532. IEEE (2019)
Coquenet, D., Chatelain, C., Paquet, T.: Span: a simple predict and align network for handwritten paragraph recognition. arXiv preprint arXiv:2102.08742 (2021)
Diem, M., Kleber, F., Fiel, S., Grüning, T., Gatos, B.: CBAD: ICDAR 2017 competition on baseline detection. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1355–1360. IEEE (2017)
Gulli, A., Pal, S.: Deep Learning with Keras. Packt Publishing Ltd., Birmingham (2017)
He, D., Cohen, S., Price, B., Kifer, D., Giles, C.L.: Multi-scale multi-task FCN for semantic page segmentation and table detection. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 254–261. IEEE (2017)
He, D., et al.: Multi-scale FCN with cascaded instance aware segmentation for arbitrary oriented word spotting in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3519–3528 (2017)
Iikura, R., Okada, M., Mori, N.: Improving BERT with focal loss for paragraph segmentation of novels. In: Dong, Y., Herrera-Viedma, E., Matsui, K., Omatsu, S., González Briones, A., Rodríguez González, S. (eds.) DCAI 2020. AISC, vol. 1237, pp. 21–30. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-53036-5_3
Kaur, R.P., Jindal, M.K., Kumar, M.: TxtLineSeg: text line segmentation of unconstrained printed text in Devanagari script. In: Singh, V., Asari, V.K., Kumar, S., Patel, R.B. (eds.) Computational Methods and Data Engineering. AISC, vol. 1257, pp. 85–100. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7907-3_7
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference for Learning Representations (ICLR) (2015)
Kosaraju, S.C., et al.: Dot-net: document layout classification using texture-based cnn. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1029–1034. IEEE (2019)
Lee, J., Hayashi, H., Ohyama, W., Uchida, S.: Page segmentation using a convolutional neural network with trainable co-occurrence features. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1023–1028. IEEE (2019)
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
Maia, A.L., Julca-Aguilar, F.D., Hirata, N.S.: A machine learning approach for graph-based page segmentation. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 424–431. IEEE (2018)
Mechi, O., Mehri, M., Ingold, R., Amara, N.E.B.: Text line segmentation in historical document images using an adaptive U-Net architecture. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 369–374. IEEE (2019)
Michael, J., Weidemann, M., Laasch, B., Labahn, R.: ICPR 2020 competition on text block segmentation on a newseye dataset. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12668, pp. 405–418. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68793-9_30
Naoum, A., Nothman, J., Curran, J.: Article segmentation in digitised newspapers with a 2D Markov model. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1007–1014. IEEE (2019)
O’Gorman, L.: The document spectrum for page layout analysis. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1162–1173 (1993)
Oktay, O., et al.: Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
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
Shiah, C.Y.: Content-based document image retrieval based on document modeling. J. Intell. Inf. Syst. 55, 287–306 (2020)
Vidal, E., et al.: The Carabela project and manuscript collection: large-scale probabilistic indexing and content-based classification. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 85–90. IEEE (2020)
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Bansal, A., Mukherjee, P., Joshi, D., Tripathi, D., Singh, A.P. (2021). Multi-task Learning for Newspaper Image Segmentation and Baseline Detection Using Attention-Based U-Net Architecture. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12917. Springer, Cham. https://doi.org/10.1007/978-3-030-86159-9_32
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