Binarization of Document Image Using Optimum Threshold Modification

Mustafa, Wan Azani; Kader, Mohamed Mydin M. Abdul

doi:10.1088/1742-6596/1019/1/012022

Cited by 39 publications

(15 citation statements)

References 17 publications

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“…Another modification of Sauvola's method applied to QR codes with an adaptive window size based on lighting conditions was recently presented by He et al [55], who used an adaptive window size partially inspired by Bernsen's approach. Another recently proposed algorithm, inspired by Sauvola's method, named WANafter the first name of one of its authors [56], focuses on low contrast document images, where the local mean values are replaced by so-called "maximum mean", being in fact the average of the mean and maximum intensity values. Nevertheless, this approach was verified only for the H-DIBCO 2016 dataset, containing 14 handwritten images; hence, it might be less suitable for machine-printed document images and OCR applications.…”

Section: Overview Of Image Binarization Algorithmsmentioning

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e.g., based on the analysis of natural images or assuming the presence of some quality degradations, such as in historical document images.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: Overview Of Image Binarization Algorithmsmentioning

confidence: 99%

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

Michalak

Okarma

2020

Sensors

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“…Binarisasi adalah proses mengubah citra aras keabuan menjadi citra hitam putih yang memiliki nilai biner, yaitu 0 atau 1 [13]. Binarisasi dilakukan dengan cara melihat nilai piksel setiap elemen pada citra aras keabuan.…”

Section: B Binarisasiunclassified

Deteksi Area Parkir Mobil Berbasis Marker Menggunakan Moment Invariants dan K-NN

Nugraha¹

2019

JuTISI

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Smart car parking system has become very important these days because it is very helpful and saves a lot of time to find a parking spot. However, the installation of that system is too expensive, then not everyone can afford it. Affordable solution for car parking spot detection can be implemented using installed camera or CCTV combined with computer vision method. New problem appears if the camera position is limited like indoor building causing one marker shape can be captured in different shape due to the different camera angle. In this work, I do a research to detecting marker using moment invariant method. Even the shape of marker changed a little, moment invariant method can still recognize that shape with overall accuracy 91.94%.

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“…Denoising is a fundamental concern for many document processing workflows, wherein unwanted artifacts introduced to a document image via noisy processes like scanning are removed. The effectiveness of the denoising stage of document processing pipelines has implications for downstream tasks like optical character recognition (OCR) and layout parsing [14,6,13,4,17,26]. Recent work in supervised machine learning has yielded promising results at the denoising task [?,6], increasing the importance of access to large volumes of high-quality training and evaluation data.…”

Section: Introductionmentioning

confidence: 99%

ShabbyPages: A Reproducible Document Denoising and Binarization Dataset

Groleau¹,

Chee²,

Larson³

et al. 2023

Preprint

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Document denoising and binarization are fundamental problems in the document processing space, but current datasets are often too small and lack sufficient complexity to effectively train and benchmark modern data-driven machine learning models. To fill this gap, we introduce ShabbyPages, a new document image dataset designed for training and benchmarking document denoisers and binarizers. ShabbyPages contains over 6,000 clean "born digital" images with synthetically-noised counterparts ("shabby pages") that were augmented using the Augraphy document augmentation tool to appear as if they have been printed and faxed, photocopied, or otherwise altered through physical processes. In this paper, we discuss the creation process of ShabbyPages and demonstrate the utility of ShabbyPages by training convolutional denoisers which remove real noise features with a high degree of human-perceptible fidelity, establishing baseline performance for a new ShabbyPages benchmark.

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Binarization of Document Image Using Optimum Threshold Modification

Cited by 39 publications

References 17 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

Deteksi Area Parkir Mobil Berbasis Marker Menggunakan Moment Invariants dan K-NN

ShabbyPages: A Reproducible Document Denoising and Binarization Dataset

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