Abstract
The automatic transcription of historical printings with OCR has made great progress in recent years. However, the correct segmentation of demanding page layouts is still challenging, in particular, the separation of text and non-text (e.g. pictures, but also decorated initials). Fully convolutional neural nets (FCNs) with an encoder-decoder structure are currently the method of choice, if suitable training material is available. Since the variation of non-text elements is huge, the good results of FCNs, if training and test material are similar, do not easily transfer to different layouts. We propose an approach based on dividing a page into many contours (i.e. connected components) and classifying each contour with a standard Convolutional neural net (CNN) as being text or non-text. The main idea is that the CNN learns to recognize text contours, i.e. letters, and classifies everything else as non-text, thus generalizing better on the many forms of non-text. Evaluations of the contour-based segmentation in comparison to classical FCNs with varying amount of training material and with similar and dissimilar test data show its effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bukhari, S.S., Al Azawi, M.I.A., Shafait, F., Breuel, T.M.: Document image segmentation using discriminative learning over connected components. In Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, pp. 183–190 (2010)
Chang, W.-Y., Chiu, C.-C., Yang, J.-H.: Block-based connected-component labeling algorithm using binary decision trees. Sensors 15(9), 23763–23787 (2015)
Chen, K., Liu, C.-L., Seuret, M., Liwicki, M., Hennebert, J., Ingold, R.: Page segmentation for historical document images based on superpixel classification with unsupervised feature learning. In: 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pp. 299–304. IEEE (2016a)
Chen, K., Seuret, M., Liwicki, M., Hennebert, J., Liu, C.-L., Ingold, R.: Page segmentation for historical handwritten document images using conditional random fields. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 90–95. IEEE (2016b)
Grüning, T., Leifert, G., Strauß, T., Michael, J., Labahn, R.: A two-stage method for text line detection in historical documents. Int. J. Doc. Anal. Recogni. (IJDAR) 22(3), 285–302 (2019)
Lafferty, J.D., McCallum, A., and Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc. (2001)
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)
Noh, W.F., Woodward, P.: SLIC (Simple Line Interface Calculation). In: Proceedings of the Fifth International Conference on Numerical Methods in Fluid Dynamics, June 28–July 2, Twente University, Enschede, Lecture Notes in Physics, vol. 59. Springer, Berlin, Heidelberg (1976). https://doi.org/10.1007/3-540-08004-X_336
Reul, C., Christ, D., Hartelt, A., Balbach, N., Wehner, M., Springmann, U., Wick, C., Grundig, C., Büttner, A., Puppe, F.: OCR4all - An open-source tool providing a(semi-)automatic OCR workflow for historical printings. Appl. Sci. 9(22), 4853 (2019)
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
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)
Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)
Wick, C., Puppe, F.: Fully convolutional neural networks for page segmentation of historical document images. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 287–292. IEEE (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fischer, N., Gehrke, A., Hartelt, A., Krug, M., Puppe, F. (2020). Contour-Based Segmentation of Historical Printings. In: Schmid, U., Klügl, F., Wolter, D. (eds) KI 2020: Advances in Artificial Intelligence. KI 2020. Lecture Notes in Computer Science(), vol 12325. Springer, Cham. https://doi.org/10.1007/978-3-030-58285-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-58285-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58284-5
Online ISBN: 978-3-030-58285-2
eBook Packages: Computer ScienceComputer Science (R0)