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U-Net-bin: hacking the document image binarization contest

P.V. Bezmaternykh1,2, D.A. Ilin1, D.P. Nikolaev1,3

Smart Engines Service LLC, 117312, Moscow, Russia,  
Federal Research Center "Computer Science and Control" of RAS, 117312, Moscow, Russia,
Institute for Information Transmission Problems of RAS, 127051, Moscow, Russia

 PDF, 3137 kB

DOI: 10.18287/2412-6179-2019-43-5-825-832

Pages: 825-832.

Full text of article: English language.

Image binarization is still a challenging task in a variety of applications. In particular, Document Image Binarization Contest (DIBCO) is organized regularly to track the state-of-the-art techniques for the historical document binarization. In this work we present a binarization method that was ranked first in the DIBCO`17 contest. It is a convolutional neural network (CNN) based method which uses U-Net architecture, originally designed for biomedical image segmentation. We describe our approach to training data preparation and contest ground truth examination and provide multiple insights on its construction (so called hacking). It led to more accurate historical document binarization problem statement with respect to the challenges one could face in the open access datasets. A docker container with the final network along with all the supplementary data we used in the training process has been published on Github.

historical document processing, binarization, DIBCO, deep learning, U-Net architecture, training dataset augmentation, document analysis.

Bezmaternykh PV, Ilin DA, Nikolaev DP. U-Net-bin: hacking the document image binarization contest. Computer Optics 2019; 43(5): 825-832. DOI: 10.18287/2412-6179-2019-43-5-825-832.

The work was partially funded by Russian Foundation for Basic Research (projects 17-29-07092 and 17-29-07093).


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