Performance Evaluation Methodology for Historical Document Image Binarization

2013 ◽  
Vol 22 (2) ◽  
pp. 595-609 ◽  
Author(s):  
K. Ntirogiannis ◽  
B. Gatos ◽  
I. Pratikakis
Keyword(s):  
Performance Evaluation ◽  
Document Image ◽  
Evaluation Methodology ◽  
Historical Document ◽  
Image Binarization ◽  
Document Image Binarization

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

2019 ◽  
Vol 43 (5) ◽  
pp. 825-832 ◽  
Author(s):  
P.V. Bezmaternykh ◽  
D.A. Ilin ◽  
D.P. Nikolaev
Keyword(s):  
Ground Truth ◽  
Document Image ◽  
Training Data ◽  
Data Preparation ◽  
Historical Document ◽  
Image Binarization ◽  
Biomedical Image ◽  
Document Image Binarization ◽  
Binarization Method ◽  
Document Binarization

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Document Image Binarization Contest (DIBCO) is organized regularly to track the state-of-the-art techniques for the historical document binarization.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 It led to more accurate historical document binarization problem statement with respect to the challenges one could face in the open access datasets.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