ICDAR2017 Competition on Document Image Binarization (DIBCO 2017)

Pratikakis, Ioannis; Zagoris, Konstantinos; Barlas, George; Gatos, Basilis

doi:10.1109/icdar.2017.228

Cited by 106 publications

(78 citation statements)

References 15 publications

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“…These images are binarized and the border noises are removed. Many techniques are proposed to binarise a document image [17], [15], [21] and remove border noises from document images [5], [18], [20], [6], [2] recently. Here we have used a recent simple and robust binarisation technique proposed by Su et.…”

Section: Experimental Results and Evalutionmentioning

confidence: 99%

A Method to Generate Synthetically Warped Document Image

Garai

Biswas

Mandal

et al. 2020

Communications in Computer and Information Science

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The digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarping of the document suffers due to the limited availability of benchmark public dataset. In recent times, deep learning based approaches are used to solve the problems accurately. To train most of the deep neural networks a large number of document images is required and generating such a large volume of document images manually is difficult. In this paper, we propose a technique to generate a synthetic warped image from a flat-bedded scanned document image. It is done by calculating warping factors for each pixel position using two warping position parameters (WPP) and eight warping control parameters (WCP). These parameters can be specified as needed depending upon the desired warping. The results are compared with similar real captured images both qualitative and quantitative way.

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Section: Experimental Results and Evalutionmentioning

confidence: 99%

A Method to Generate Synthetically Warped Document Image

Garai

Biswas

Mandal

et al. 2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

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“…None of these images were available to the participants before their publication. The final results and all the measurements were presented in [7].…”

Section: Resultsmentioning

confidence: 99%

“…In this work we explain in detail the binarization method submitted to the DIBCO'17 that won in both machine-printed and handwritten categories among 26 evaluated algorithms [7]. We chose a CNN based approach using U-Net architecture [8] because of its ability to process big image patches capturing their contexts.…”

mentioning

confidence: 99%

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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“…Twenty-three binarization algorithms were tested using the methodology described: [34] A ground-truth image for each "real" world one is needed to allow a quantitative assessment of the quality of the final binary image. Only the DIBCO dataset [10] had ground-truth images available. This makes the assessment task of real-world images extremely difficult [35].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The development of new binarization algorithms is still an important research topic. International competitions on binarization algorithms, such as DIBCO -Document Image Binarization Competition [10], are an evidence of the relevance of this area. This paper presents a new global filter [1] to binarize documents, which is able to remove the back-to-front noise in a wide range of documents.…”

Section: Introductionmentioning

confidence: 99%

A New Binarization Algorithm for Historical Documents

et al. 2018

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Abstract:Monochromatic documents claim for much less computer bandwidth for network transmission and storage space than their color or even grayscale equivalent. The binarization of historical documents is far more complex than recent ones as paper aging, color, texture, translucidity, stains, back-to-front interference, kind and color of ink used in handwriting, printing process, digitalization process, etc. are some of the factors that affect binarization. This article presents a new binarization algorithm for historical documents. The new global filter proposed is performed in four steps: filtering the image using a bilateral filter, splitting image into the RGB components, decision-making for each RGB channel based on an adaptive binarization method inspired by Otsu's method with a choice of the threshold level, and classification of the binarized images to decide which of the RGB components best preserved the document information in the foreground. The quantitative and qualitative assessment made with 23 binarization algorithms in three sets of "real world" documents showed very good results.

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ICDAR2017 Competition on Document Image Binarization (DIBCO 2017)

Cited by 106 publications

References 15 publications

A Method to Generate Synthetically Warped Document Image

A Method to Generate Synthetically Warped Document Image

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

A New Binarization Algorithm for Historical Documents

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