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

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

Автор: Bezmaternykh Pavel Vladimirovich, Ilin Dmitrii Alexeevich, Nikolaev Dmitry Petrovich

Журнал: Компьютерная оптика @computer-optics

Рубрика: Обработка изображений, распознавание образов

Статья в выпуске: 5 т.43, 2019 года.

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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Document Image Binarization Contest (DIBCO) is organized regularly to track the state-of-the-art techniques for the historical document binarization.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 It led to more accurate historical document binarization problem statement with respect to the challenges one could face in the open access datasets.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.

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Historical document processing, binarization, dibco, deep learning, u-net architecture, training dataset augmentation, document analysis

Короткий адрес: https://sciup.org/140246518

IDR: 140246518   |   DOI: 10.18287/2412-6179-2019-43-5-825-832

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