Abstract
The binarization step for old documents is still a challenging task even though many hand-engineered and deep learning algorithms have been offered. In this research work, we address foreground and background segmentation using a convolutional autoencoder network with 3 supporting components. The assessment of several hyper-parameters including the window size, the number of convolution layers, the kernel size, the number of filters as well as the number of encoder-decoder layers on the network is conducted. In addition, the skip connections approach is considered in the decoding procedure. Moreover, we evaluated the summation and concatenation function before the up-sampling process to reuse the previous low-level feature maps and to enrich the decoded representation. Based on several experiments, we determined that kernel size, the number of filters, and the number of encoder-decoder blocks have a little impact in term of binarization performance. While the window size and multiple convolutional layers are more impactful than other hyper-parameters. However, they require more storage and may increase computation costs. Moreover, a careful embedding of batch normalization and dropout layers also provides a contribution to handle overfitting in the deep learning model. Overall, the multiple convolutional autoencoder network with skip connection successfully enhances the binarization accuracy on old Sundanese palm leaf manuscripts compared to preceding state of the art methods.
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References
Sulaiman, A., Omar, K., Nasrudin, M.F.: Degraded historical document binarization: a review on issues, challenges, techniques, and future directions. J. Imaging 5(4), 48 (2019)
Tensmeyer, C., Martinez, T.: Historical document image binarization: a review. SN Comput. Sci. 1(3), 1–26 (2020). https://doi.org/10.1007/s42979-020-00176-1
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Niblack, W.: An Introduction to Digital Image Processing. Prentice-Hall, Englewood Cliffs (1986)
Bernsen, J.: Dynamic thresholding of gray level image. In: ICPR 1986: Proceedings of International Conference on Pattern Recognition, Berlin, pp. 1251–1255 (1986)
Kesiman, M.W.A., et al.: Benchmarking of document image analysis tasks for palm leaf manuscripts from Southeast Asia. J. Imaging 4(2), 43 (2018)
Howe, N.R.: Document binarization with automatic parameter tuning. Int. J. Doc. Anal. Recognit. 16(3), 247–258 (2013)
Mesquita, R.G., Silva, R.M.A., Mello, C.A.B., Miranda, P.B.C.: Parameter tuning for document image binarization using a racing algorithm. Expert Syst. Appl. 42(5), 2593–2603 (2015)
Sauvola, J., Seppanen, T., Haapakoski, S., Pietikainen, M.: Adaptive document binarization. In: Proceedings of the Fourth ICDAR, pp. 147–152 (1997)
Pastor-Pellicer, J., España-Boquera, S., Zamora-Martínez, F., Afzal, M.Z., Castro-Bleda, M.J.: Insights on the use of convolutional neural networks for document image binarization. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2015. LNCS, vol. 9095, pp. 115–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19222-2_10
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)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)
Tensmeyer, C., Martinez, T.: Document image binarization with fully convolutional neural networks. In: 2017 14th IAPR ICDAR, pp. 99–104 (2017)
Peng, X., Cao, H., Natarajan, P.: Using convolutional encoder-decoder for document image binarization. In: 2017 14th IAPR ICDAR, pp. 708–713 (2017)
Calvo-Zaragoza, J., Gallego, A.J.: A selectional auto-encoder approach for document image binarization. Pattern Recognit. 86, 37–47 (2019)
Lazzara, G., Levillain, R., Geraud, T., Jacquelet, Y., Marquegnies, J., Crepin-Leblond, A.: The SCRIBO module of the Olena platform: a free software framework for document image analysis. In: 2011 ICDAR, pp. 252–258 (2011)
Pratikakis, I., Zagoris, K., Barlas, G., Gatos, B.: ICFHR 2016 handwritten document image binarization contest (H-DIBCO 2016). In: Proceedings of the International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 619–623 (2016)
Burie, J.C., et al.: ICFHR2016 competition on the analysis of handwritten text in images of balinese palm leaf manuscripts. In: 2016 15th ICFHR, pp. 596–601 (2016)
Géron, A.: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow?: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly, Sebastopol (2019)
Feng, X., Jonathan, W., Q.M., Yang, Y., Cao, L.: An autuencoder-based data augmentation strategy for generalization improvement of DCNNs. Neurocomputing 402, 283–297 (2020)
Ribeiro, M., Lazzaretti, A.E., Lopes, H.S.: A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognit. Lett. 105, 13–22 (2018)
Heger, J., Desai, G., Zein El Abdine, M.: Anomaly detection in formed sheet metals using convolutional autoencoders. In: Procedia CIRP, pp. 1281–1285. Elsevier B.V. (2020)
Mao, X.-J., Shen, C., Yang, Y.-B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 2810–2818. Curran Associates Inc., Red Hook (2016)
Xia, M., Liu, X., Wong, T.T.: Invertible grayscale. ACM Trans. Graph. 37, 1–10 (2018)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: 2nd International Conference on Learning Representations, ICLR 2014, Canada, pp. 1–14 (2014)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. In: ICLR-2015 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on CVPR, pp. 2261–2269 (2017)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on CVPR, pp. 2818–2826 (2016)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: The 32nd International Conference on Machine Learning, Lille, France (2015)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 4278–4284. AAAI Press (2017)
Sun, S., Pang, J., Shi, J., Yi, S., Ouyang, W.: FishNet: a versatile backbone for image, region, and pixel level prediction. In: 32nd Conference on Neural Information Processing Systems, pp. 754–764 (2018)
Thakkar, V., Tewary, S., Chakraborty, C.: Batch normalization in convolutional neural networks - a comparative study with CIFAR-10 data. In: 2018 Fifth International Conference on Emerging Applications of Information Technology, pp. 1–5 (2018)
Garbin, C., Zhu, X., Marques, O.: Dropout vs. batch normalization: an empirical study of their impact to deep learning. Multimed. Tools Appl. 79, 12777–12815 (2020)
Kesiman, M.W.A., et al.: ICFHR 2018 competition on document image analysis tasks for southeast Asian palm leaf manuscripts. In: Proceedings of ICFHR, pp. 483–488. IEEE (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)
Pastor-Pellicer, J., Zamora-Martínez, F., España-Boquera, S., Castro-Bleda, M.J.: F-measure as the error function to train neural networks. In: Rojas, I., Joya, G., Gabestany, J. (eds.) IWANN 2013. LNCS, vol. 7902, pp. 376–384. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38679-4_37
Acknowledgments
We would like to thank Riki Nawawi and Undang Darsa as Sundanese philologists for their fruitful discussions regarding old Sundanese manuscripts. This work is supported by the Directorate General of Higher Education, Ministry of Education, Culture, Research and Technology, the Republic of Indonesia through the funding of the BPPLN-DIKTI Indonesian Scholarship Program.
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Paulus, E., Burie, JC., Verbeek, F.J. (2021). Binarization Strategy Using Multiple Convolutional Autoencoder Network for Old Sundanese Manuscript Images. 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_10
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