An Active Contour Based Method for Image Binarization: Application to Degraded Historical Document Images

Hadjadj, Zineb; Maziane, Abdelkrim; Cheriet, Mohamed; Cherfa, Yazid

doi:10.1109/icfhr.2014.115

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…Some binarization methods try to incorporate global or local approaches, like [9][10][11][12]16]. Certain methods also have incorporated background estimation and normalization steps, like [11][12][13].…”

Section: Overviewmentioning

confidence: 99%

ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

Hadjadj

Meziane

Cherfa

et al. 2016

Lecture Notes in Computer Science

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Section: Overviewmentioning

confidence: 99%

ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

Hadjadj

Meziane

Cherfa

et al. 2016

Lecture Notes in Computer Science

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“…University of Washington dataset (UW-III) 4 is the nonhistorical English/technical document image database produced by the Intelligent Systems Laboratory, at the University of Washington, Seattle, Washington, USA. …”

Section: A Historical Datasets -Some Examplesmentioning

confidence: 99%

Marginal Noise Reduction in Historical Handwritten Documents -- A Survey

Chakraborty

Blumenstein

2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

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Abstract-This paper presents a survey on different approaches for removing the marginal noise from document images, and anlaysing the research challenges of those methods relating to handwritten historical datasets. In this survey, historical documents collected from Australian Archives and Libraries are introduced and the associated layout complexities of those document images are also described. Benchmarking other historical databases related to this work is also discussed. This survey discusses the difficulties and suitability of the state-of-the-art methods to remove marginal noise as well as preserving the text content from handwritten historical documents. This survey helps researchers to identify appropriate methods according to the associated marginal noise and also illustrates their drawbacks in order to make suggestions for developing approaches, which are more general and robust for any datasets.

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Degraded Document Image Binarization Using Active Contour Model

Gupta

Bag

2021

Communications in Computer and Information Science

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Document image binarization separates the foreground from background which is a very crucial pre-processing step in OCR. Accuracy of binarization immensely influences the accuracy of OCR. Various degradations like inadequate illumination, complex background, ink bleed, smear, etc. make binarization a challenging exercise. In this paper, we propose a novel method for binarization based on Active Contour Model (ACM) which is different from current thresholding techniques. Instead of calculating a global or local threshold value, it uses the energyminimization concept of ACM to accomplish binarization. Background of the document image is estimated adaptively and then removed to handle noises in degraded documents. Active contour model is then applied to the image to get the binary output. Comprehensive experimentation has been completed effectively with benchmark DIBCO series datasets. A comparison with other existing methods verifies the efficiency of the proposed method.

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An Active Contour Based Method for Image Binarization: Application to Degraded Historical Document Images

Cited by 6 publications

References 26 publications

ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

Marginal Noise Reduction in Historical Handwritten Documents -- A Survey

Degraded Document Image Binarization Using Active Contour Model

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