Image Enhancement Background for High Damage Malay Manuscripts using Adaptive Threshold Binarization

Yahya, Sitti Rachmawati; Omar, Khairuddin; Abdullah, Siti Norul Huda Sheikh; Sophian, Ali

doi:10.18517/ijaseit.8.4-2.6958

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…If the image was directly processed by the adaptive threshold algorithm, the entire main body will have a more serious undersegmentation (Figure 4(e)). In order to avoid the above problems, this paper introduces an adaptive threshold binarization segmentation algorithm, calculates the threshold value for each pixel separately, and averages the n × n pixels around the pixel point Pði, jÞ to obtain the threshold value F; then, the result of subtracting the threshold offset C from F is used as the threshold of the pixel [30], the schematic diagram of the binarization threshold was shown in Figure 5 which is…”

Section: Pixel Extraction Of the Main Body In Cot Imagementioning

confidence: 99%

Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

Liu

Zhang

Zhou

et al. 2021

International Journal of Photoenergy

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The microstructure is the key factor for quality discriminate of coke. In view of the characteristics of coke optical tissue (COT), a segmentation method of coke microstructures based on adaptive clustering was proposed. According to the strategy of multiresolution, adaptive threshold binarization and morphological filtering were carried out on COT images with lower resolution. The contour of the COT body was detected through the relationship checking between contours in the binary image, and hence, COT pixels were picked out to cluster for tissue segmentation. In order to get the optimum segmentation for each tissue, an advanced K -means method with adaptive clustering centers was provided according to the Calinski-Harabasz score. Meanwhile, Euclidean distance was substituted with Mahalanobis distance between each pixel in HSV space to improve the accuracy. The experimental results show that compared with the traditional K -means algorithm, FCM algorithm, and Meanshift algorithm, the adaptive clustering algorithm proposed in this paper is more accurate in the segmentation of various tissue components in COT images, and the accuracy of tissue segmentation reaches 94.3500%.

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Section: Pixel Extraction Of the Main Body In Cot Imagementioning

confidence: 99%

Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

Liu

Zhang

Zhou

et al. 2021

International Journal of Photoenergy

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“…The DRD k represents the deformity of the flipped k th pixel. Its weighted sum is equal to the ground truth image of 5x5 block, but it differs from the flipped k th pixel at (x, y) as shown in (10).…”

Section: B Setup Of the Experimentsmentioning

confidence: 99%

“…Although no binarization methods are yet to be effective for different types of document degradations, the binarization of heavily degraded document images remains under research [9]. Some researchers have proposed binarization methods that are dependent on many stages [10]. They use a preprocessing stage before binarization; these methods utilize some filters to eliminate noises in the images before the binarization step.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Phase-Based Binarization Method for Degraded Historical Documents

Sulaiman

Omar

Nasrudin

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Document image binarization is the first essential step in digitalizing images and is considered an essential technique in both document image analysis applications and optical character recognition operations, the binarization process is used to obtain a binary image from the original image, binary image is the proper presentation for image segmentation, recognition, and restoration as underlined by several studies which assure that the next step of document image analysis applications depends on the binarization result. However, old and historical document images mainly suffering from several types of degradations, such as bleeding through the blur, uneven illumination and other types of degradations which makes the binarization process a difficult task. Therefore, extracting of foreground from a degraded background relies on the degradation, furthermore it also depends on the type of used paper and document age. Developed binarization methods are necessary to decrease the impact of the degradation in document background. To resolve this difficulty, this paper proposes an effective, enhanced binarization technique for degraded and historical document images. The proposed method is based on enhancing an existing binarization method by modifying parameters and adding a post-processing stage, thus improving the resulting binary images. This proposed technique is also robust, as there is no need for parameter tuning. After using document image binarization Contest (DIBCO) datasets to evaluate this proposed technique, our findings show that the proposed method efficiency is promising, producing better results than those obtained by some of the winners in the DIBCO.

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Synthesizing Retinal Images using End-To-End VAEs-GAN Pipeline-Based Sharpening and Varying Layer

Saeed,

Sheikh Abdullah,

Che-Hamzah

et al. 2023

Multimed Tools Appl

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This study attempts to synthesize a realistic-looking fundus image from a morphologically changed vessel structure using the newly proposed sharpening and varying vessels technique (SVV). This technique sharpens the reconstructed vessels and introduces variation to their structure to generate multiple images from a single input mask. This helps to reduce the reliance on expensive and scarce annotated medical data. The study also aims to overcome the limitations of current methods, such as unrealistic optic disc boundaries, extreme vessel tortuosity, and missed optic discs. This is mainly due to the fact that existing models penalize their weights based on the difference between real and synthetic images using only a single mask. Therefore, their emphasis is on generating the input mask while disregarding other important fundoscopic features. Inspired by the recent progress in Generative Adversarial Nets (GANs) and Variational Autoencoder (VAE), the proposed approach was able to preserve the geometrical shape of critical fundus characteristics. Visual and quantitative results indicate that the produced images are considerably distinct from the ones used for training. However, they also exhibit anatomical coherence and a reasonable level of visual. The data utilized in this study and the programming code necessary to recreate the experiment can be accessed at https://github.com/AliSaeed86/SVV_GAN.

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Image Enhancement Background for High Damage Malay Manuscripts using Adaptive Threshold Binarization

Cited by 6 publications

References 22 publications

Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

An Efficient Phase-Based Binarization Method for Degraded Historical Documents

Synthesizing Retinal Images using End-To-End VAEs-GAN Pipeline-Based Sharpening and Varying Layer

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