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

Michalak, Hubert; Okarma, Krzysztof

doi:10.3390/s20102914

Cited by 17 publications

(10 citation statements)

References 81 publications

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“…the image at the bottom row of 6(c). Additionally, in figure 7(b) we show the performance of our method in challenging multi-illuminant conditions with shadows and shadings available in a recent OCR dataset [33]. We can see the results of after our white-balancing (WBNet) in the WB column of 7(b), and in column SMR we can see how SMTNet gracefully handles strong illumination conditions in real scenarios.…”

Section: Shadow Removal and Non-uniform Illuminationmentioning

confidence: 96%

Intrinsic Decomposition of Document Images In-the-Wild

Das,

Sial,

et al. 2020

Preprint

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Automatic document content processing is affected by artifacts caused by the shape of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 26% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW.

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Section: Shadow Removal and Non-uniform Illuminationmentioning

confidence: 96%

Intrinsic Decomposition of Document Images In-the-Wild

Das,

Sial,

et al. 2020

Preprint

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“…An approach for binarization of non-uniformly illuminated document images to accurately recognize alphanumerical characters is presented in [53]. The proposed method combines local and global thresholding methods, i.e., Sauvola and Otsu methods to achieve robust binarization and improved performance compared to existing binarization methods.…”

Section: Binarization and Thinningmentioning

confidence: 99%

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

et al. 2023

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Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.

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“…White pixels exceed the barrier, but black pixels do not. The experiments aim to verify feasible combinations of the newly presented techniques [6,7,8] with alternative algorithms that do not require a priori training, excluding some deep learning techniques because of memory and technology limitations. A Digital Single Lens Reflex (DSLR) camera, the Nikon N70, is used to take the pictures in order to lessen the direct impact of camera specs and qualities on the features of the produced picture and additional processing operations.…”

Section: Binarizationmentioning

confidence: 99%

An approach for improving Optical Character Recognition using Contrast enhancement technique

Shine

Bhutani²,

Keerthana³

et al. 2023

J. Phys.: Conf. Ser.

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Digital cameras and mobile image capture of documents are two examples of new developments in the fields of optical character recognition and text recognition. Scans of text or text photographic images and even natural photography results can be distorted to the point where OCR digitization is inaccurate. It offers a unique non-parametric unattended approach to correct unwanted document image distortions to achieve optimal OCR accuracy. It applies a highly effective stack of document image enhancement algorithms to restore perfect images distorted by unknown sources of distortion. First, it provides a means of modifying local brightness and contrast in order to better handle different illumination levels and atypical light transmission patterns in the image. Then apply a nifty grayscale conversion method to your photo to give it a new look. Third, it uses unsharp masking techniques to further enhance important details in grayscale images. Finally, we use the best global binarization technique to prepare the final document image for OCR recognition. The proposed technique has the potential to significantly improve the text recognition rate and accuracy of optical character recognition.

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

Cited by 17 publications

References 81 publications

Intrinsic Decomposition of Document Images In-the-Wild

Intrinsic Decomposition of Document Images In-the-Wild

A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

An approach for improving Optical Character Recognition using Contrast enhancement technique

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