Document Image Binarization Technique for Degraded Document Images

Lokhande, Supriya; Dawande, N.A.

doi:10.5120/21858-5183

Cited by 1 publication

(2 citation statements)

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“…However, it should be noted that the edges detected by the edge detection technique may not completely enclose the prospective text area, thus requiring further improvement. Holambe et al [33] exploited adaptive image contrast in combination with Canny's edge diagram to identify the edge pixels of the font. Jia et al [34] used structural symmetry pixels (SSPs) to calculate local thresholds for the neighborhood.…”

Section: Image Feature Methodsmentioning

confidence: 99%

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Summary of Document Image Binarization

Yang,

Zhang,

Zhou

et al. 2024

Preprint

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Document image binarization is a challenging task, especially when it comes to text segmentation in degraded document images. The binarization, as a pre-processing step of optical character recognition (OCR), is one of the most fundamental and commonly used segmentation methods. It separates the foreground text from the background of the document image to facilitate subsequent image processing. In view of the different degradation degree of document image, researchers have proposed a variety of solutions. This paper reviews the main binarization techniques, including both traditional algorithms and deep learning-based algorithms. We also summarize some difficulties and challenges in the field of document image binarization. Here, we evaluate various image binarization techniques to identify shortcomings in current methods and provide some help for future research.

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Section: Image Feature Methodsmentioning

confidence: 99%

“…T.Lelore [32] FAIR algorithm based on edge detection to locate text. Holambe [33] Adaptive image contrast combined with Canny's edge map to Edge detection identify stroke-edge pixels. Jia [34] Use structural symmetric pixels (SSPs) to calculate local thresholds in the neighborhood.…”

Section: Classification Algorithm Descriptionmentioning

confidence: 99%