Kombinasi Metode Nilai Ambang Lokal dan Global untuk Restorasi Dokumen Jawi Kuno

Saddami, Khairun; Arnia, Fitri; Away, Yuwaldi; Munadi, Khairul

doi:10.25126/jtiik.2020701741

Cited by 4 publications

(8 citation statements)

References 13 publications

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“…The ultimate binarization is achieved by merging the findings of these two branches. Saddami et al [56] employed an integrated technique such as local and global thresholding methods to extract text from the backdrop to recover the information on degraded ancient Jawi manuscripts. P. Ranjitha et al [57] suggested a classification system to deal with degraded document photographs by blending the modification of local and global binarization algorithms.…”

Section: Mixed Threshold Methodsmentioning

confidence: 99%

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A Review of Document Binarization: Main Techniques, New Challenges, and Trends

Yang,

Zuo,

Zhou

et al. 2024

Electronics

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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 degrees of document images, researchers have proposed a variety of solutions. In this paper, we have summarized some challenges and difficulties in the field of document image binarization. Approximately 60 methods documenting image binarization techniques are mentioned, including traditional algorithms and deep learning-based algorithms. Here, we evaluated the performance of 25 image binarization techniques on the H-DIBCO2016 dataset to provide some help for future research.

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Section: Mixed Threshold Methodsmentioning

confidence: 99%

“…Saddami [56] It combines the Niblack and Wolf methods. The Otsu method and standard deviation are combined.…”

Section: Kntirogiannis [53]mentioning

confidence: 99%

A Review of Document Binarization: Main Techniques, New Challenges, and Trends

Yang,

Zuo,

Zhou

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…The ultimate binarization is achieved by merging the findings of these two branches. Saddami et al [22] employed an integrated technique such as local and global thresholding methods to extract text from the backdrop to recover the information on degraded ancient Jawi manuscripts. P. Ranjitha et al [23] suggested a classification system to deal with degraded document photographs by blending the modification of local and global binarization algorithms.…”

Section: Mixed Threshold Methodsmentioning

confidence: 99%

Summary of Document Image Binarization

Yang,

Zhang,

Zhou

et al. 2024

Preprint

View full text Add to dashboard Cite

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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“…Consequently, alternative methodologies have been proposed to mitigate the limitations inherent in employing a fixed threshold. Noteworthy among these are Niblack [21], Sauvola [22], Wolf [23], and others [24][25][26][27][28][29][30][31][32][33][34][35].…”

Section: Traditional Binarization Techniquesmentioning

confidence: 99%

“…Many approaches have been proposed to realize document image binarization. The typical methods include thresholding methods, such as the Otsu algorithm [20], Niblack's method [21], Sauvola's method [22], Wolf's method [23], among others [24][25][26][27][28][29][30][31][32][33][34][35]. There are also other traditional algorithms based on edge detection [36][37][38][39][40], and others utilizing fuzzy logic [41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Transformer-CNN Network for Document Image Binarization

Zhang,

Wang,

Wan

2024

Preprint

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Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

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Kombinasi Metode Nilai Ambang Lokal dan Global untuk Restorasi Dokumen Jawi Kuno

Cited by 4 publications

References 13 publications

A Review of Document Binarization: Main Techniques, New Challenges, and Trends

A Review of Document Binarization: Main Techniques, New Challenges, and Trends

Summary of Document Image Binarization

An Efficient Transformer-CNN Network for Document Image Binarization

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