BinarizationShop

Deng, Fanbo; Wu, Zheng Yi; Zheng, Lu; Brown, Michael S.

doi:10.1145/1816123.1816161

Cited by 28 publications

(10 citation statements)

References 12 publications

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“…The experimental verification of the proposed combined image binarization method for the OCR purposes should be conducted using a database of unevenly illuminated document images, for which the ground truth text data are known. Unfortunately, currently available image databases, such as the DIBCO [4], Bickley Diary [90], or Nabuco datasets [87], used for the performance analysis of image binarization methods contain usually a handwritten text (in some cases, also machine-printed) subjected to some distortions such as ink fading, the presence of some stains, or some other local distortions.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition

Michalak

Okarma

2020

Sensors

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Image binarization is one of the key operations decreasing the amount of information used in further analysis of image data, significantly influencing the final results. Although in some applications, where well illuminated images may be easily captured, ensuring a high contrast, even a simple global thresholding may be sufficient, there are some more challenging solutions, e.g., based on the analysis of natural images or assuming the presence of some quality degradations, such as in historical document images. Considering the variety of image binarization methods, as well as their different applications and types of images, one cannot expect a single universal thresholding method that would be the best solution for all images. Nevertheless, since one of the most common operations preceded by the binarization is the Optical Character Recognition (OCR), which may also be applied for non-uniformly illuminated images captured by camera sensors mounted in mobile phones, the development of even better binarization methods in view of the maximization of the OCR accuracy is still expected. Therefore, in this paper, the idea of the use of robust combined measures is presented, making it possible to bring together the advantages of various methods, including some recently proposed approaches based on entropy filtering and a multi-layered stack of regions. The experimental results, obtained for a dataset of 176 non-uniformly illuminated document images, referred to as the WEZUT OCR Dataset, confirm the validity and usefulness of the proposed approach, leading to a significant increase of the recognition accuracy.

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Section: Discussion Of the Resultsmentioning

confidence: 99%

Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition

Michalak

Okarma

2020

Sensors

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“…To validate the effectiveness of the proposed method and especially its impact on highly degraded images that have suffered different types of degradation, we have not only experimented the method on two groups of images "Handwritten & Printed" belonging to different databases on DIBCO in [26]- [28], but also on the database BICKLEY DIARY [29] known for its highly deteriorating document images. We also compared the performance of the proposed method with that of the conventional binarization methods, recent methods of the state-of-the-art and the top ranking algorithms in each competition of DIBCO.…”

Section: Resultsmentioning

confidence: 99%

Section: B Test On Bickley Diary Datasetmentioning

confidence: 99%

“…To further promote our method and its effectiveness in highly degraded document images, we make the test on BICKLEY DIARY [29] dataset that contains a set of highly degraded document images suffering from different types of degradation, such as water stains and transparent ink. Hence, BICKLEY DIARY dataset [29] is more difficult than the DIBCO dataset from the binarization point of view. In order to compare our method with the methods recently reported in [8], [9], [11], [33], we take a set of seven degraded images and their corresponding reference images that are manually annotated using Pix Labeler [37].…”

Section: B Test On Bickley Diary Datasetmentioning

confidence: 99%

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Improved Degraded Document Image Binarization Using Median Filter for Background Estimation

Khitas¹,

Ziet²,

Bouguezel³

2018

ElAEE

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In this paper, we propose a new binarization method suitable for images having a variety of sizes and degradation levels. It is mainly based on the idea of estimating a document background surface by new smoothing approach through a median filter and compute-Sharp-Peak to replace iterative polynomial. The resulting document image is then segmented by a global threshold binarization. The simulation results confirm that the performance of the proposed method is generally competitive to that of the existing methods. For highly degraded document images, specifically the documents in the BICKLEY DIARY data-base, the performance of the former is substantially better than that of the latter. We show how this approach outperforms the existing and widely used binarization methods in terms of accuracy, F-measure, PSNR, NRM, MPM, DRD, recall and precision.

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“…We evaluated our proposed method on ten public datasets from the document image binarization competitions: DIBCO'09 [7], H-DIBCO '10 [29], DIBCO'11 [30], H-DIBCO'12 [31],DIBCO'13 [32], H-DIBCO'14 [27], H-DIBCO'16 [34], DIBCO'17 [35], H-DIBCO'18 [33], DIBCO'19 [36]. The training set also includes document images from the Bickley Diary dataset [5], Persian Heritage Image Binarization Dataset (PHIDB) [23], the Synchromedia Multispectral dataset (S-MS) [11]. Inspired by [10,52], all other datasets are utilized for training when evaluating a specific (H-)DIBCO dataset.…”

Section: Experiments 41 Datasets and Evaluation Metricsmentioning

confidence: 99%

GDB: Gated convolutions-based Document Binarization

Yang¹,

Xiong²,

Wu³

2023

Preprint

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Document binarization is a key pre-processing step for many document analysis tasks. However, existing methods can not extract stroke edges finely, mainly due to the fairtreatment nature of vanilla convolutions and the extraction of stroke edges without adequate supervision by boundaryrelated information. In this paper, we formulate text extraction as the learning of gating values and propose an endto-end gated convolutions-based network (GDB) to solve the problem of imprecise stroke edge extraction. The gated convolutions are applied to selectively extract the features of strokes with different attention. Our proposed framework consists of two stages. Firstly, a coarse sub-network with an extra edge branch is trained to get more precise feature maps by feeding a priori mask and edge. Secondly, a refinement sub-network is cascaded to refine the output of the first stage by gated convolutions based on the sharp edge. For global information, GDB also contains a multiscale operation to combine local and global features. We conduct comprehensive experiments on ten Document Image Binarization Contest (DIBCO) datasets from 2009 to 2019. Experimental results show that our proposed methods outperform the state-of-the-art methods in terms of all metrics on average and achieve top ranking on six benchmark datasets.

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BinarizationShop

Cited by 28 publications

References 12 publications

Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition

Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition

Improved Degraded Document Image Binarization Using Median Filter for Background Estimation

GDB: Gated convolutions-based Document Binarization

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