Skeletonization of Binary Images and Finding of Singular Points for Fingerprint Recognition

Гудков, Владимир Юльевич; Клюев, Даниил Александрович

doi:10.14529/ctcr150302

CTAC&R

2015

DOI: 10.14529/ctcr150302

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Skeletonization of Binary Images and Finding of Singular Points for Fingerprint Recognition

Владимир Юльевич Гудков¹,

Даниил Александрович Клюев²

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2019

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

Bezmaternykh¹,

Ilin²,

Nikolaev

2019

Computer Optics

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Document Image Binarization Contest (DIBCO) is organized regularly to track the state-of-the-art techniques for the historical document binarization.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 It led to more accurate historical document binarization problem statement with respect to the challenges one could face in the open access datasets.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

Bezmaternykh¹,

Ilin²,

Nikolaev

2019

Computer Optics

View full text Add to dashboard Cite

show abstract

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Skeletonization of Binary Images and Finding of Singular Points for Fingerprint Recognition

Cited by 1 publication

References 0 publications

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

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

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