Bipolar morphological U-Net for document binarization

2021 ◽  
Author(s):  
Elena E. Limonova ◽  
Dmitry P. Nikolaev ◽  
Vladimir V. Arlazarov
Keyword(s):  
Document Binarization

A learning framework for the optimization and automation of document binarization methods

2013 ◽  
Vol 117 (3) ◽  
pp. 269-280 ◽  
Author(s):  
Mohamed Cheriet ◽  
Reza Farrahi Moghaddam ◽  
Rachid Hedjam
Keyword(s):  
Learning Framework ◽  
Document Binarization

scholarly journals A Hybrid Swarm Optimization Approach for Document Binarization

2019 ◽  
Vol 28 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Mohamed ABD ELFATTAH ◽  
Aboul Ella HASSANIEN ◽  
Sherihan ABUELENIN
Keyword(s):  
Optimization Approach ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Document Binarization

Document image binarization using difference of concatenated convolutions

2021 ◽  
pp. 1-14
Author(s):  
R.L. Jyothi ◽  
M. Abdul Rahiman
Keyword(s):  
Parameter Tuning ◽  
Document Image ◽  
Document Images ◽  
Historical Document ◽  
Proposed Model ◽  
Segmentation Methods ◽  
Document Image Binarization ◽  
Low Pass ◽  
Degraded Document ◽  
Document Binarization

Binarization is the most important stage in historical document image processing. Efficient working of character and word recognition algorithms depend on effective segmentation methods. Segmentation algorithms in turn depend on images free of noises and degradations. Most of these historical documents are illegible with degradations like bleeding through degradation, faded ink or faint characters, uneven illumination, contrast variation, etc. For effective processing of these document images, efficient binarization algorithms should be devised. Here a simple modified version of the Convolutional Neural Network (CNN) is proposed for historical document binarization. AOD-Net architecture for generating dehazed images from hazed images is modified to create the proposed network.The new CNN model is created by incorporating Difference of Concatenation layer (DOC), Enhancement layer (EN) and Thresholding layer into AOD-Net to make it suitable for binarization of highly degraded document images. The DOC layer and EN layer work effectively in solving degradation that exists in the form of low pass noises. The complexity of working of the proposed model is reduced by decreasing the number of layers and by introducing filters in convolution layers that work with low inter-pixel dependency. This modified version of CNN works effectively with a variety of highly degraded documents when tested with the benchmark historical datasets. The main highlight of the proposed network is that it works efficiently in a generalized manner for any type of document images without further parameter tuning. Another important highlight of this method is that it can handle most of the degradation categories present in document images. In this work, the performance of the proposed model is compared with Otsu, Sauvola, and three recent Deep Learning-based models.

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