Improvement of binarization performance by applying DCT as pre-processing procedure

Arnia, Fitri; Munadi, Khairul; Fardian,; Muchallil, Sayed

doi:10.1109/isccsp.2014.6877832

Cited by 13 publications

(5 citation statements)

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“…Many pre-processing procedures have been proposed for improving binarization performance such as discrete cosine transform [15], background estimation [9], and contrast construction [16]. In our proposed method, we performed histogram analysis to estimate how a pixel is deployed from an image.…”

Section: Proposed Methodsmentioning

confidence: 99%

Effective and fast binarization method for combined degradation on ancient documents

Saddami

Munadi

Away

et al. 2019

Heliyon

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Document image binarization is a challenging task because of combined degradation in a document. In this study, a new binarization method is proposed for binarizing an ancient document with combined degradation. The proposed method comprises the following four stages: histogram analysis, contrast enhancement, local adaptive thresholding, and artifact removal. In histogram analysis, a new approach is applied to establish a uniform background. Next, the image contrast is enhanced using a new contrast enhancement, and then the document is binarized using a novel local adaptive thresholding. Artifacts from the binarization process are removed in the artifact removal stage. Finally, an experiment is conducted using one private and four public datasets and by simulating the proposed method with and without contrast enhancement. The results showed that the proposed method is faster and more effective compared to other state-of-the-art procedures for binarizing ancient documents.

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Section: Proposed Methodsmentioning

confidence: 99%

Effective and fast binarization method for combined degradation on ancient documents

Saddami

Munadi

Away

et al. 2019

Heliyon

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“…where f (n 1 , n 2 ), with 0 ≤ n 1 < N 1 , and 0 ≤ n 2 < N 2 is spatial image and F c (k 1 , k 2 ), with 0 ≤ k 1 < N 1 , and 0 ≤ k 2 < N 2 is DCT coefficient, C(k) is gain control,f c (n 1 , n 2 ) with 0 ≤ n 1 < N 1 , and 0 ≤ n 2 < N 2 is the spatial image that is reproduced from a small number of k 1 and k 2 , (denoted aŝ F c (k 1 , k 2 )), with 0 ≤ k 1 <N 1 , and 0 ≤ k 2 <N 2 , wherein N 1 is less than N 1 andN 2 is less than N 2 [18] C. GLCM of Images with Limited DCT Coefficients…”

Section: B Discrete Cosine Transform (Dct)mentioning

confidence: 99%

Real time textile defect detection using GLCM in DCT-based compressed images

Arnia

Munadi

2015

2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)

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This paper proposes a framework for textile defect detection on the basis of energy and contrast features of the graylevel co-occurrence matrix (GLCM). We calculate these features in DCT-based compressed image (DCTb-I), which was produced by using only a small number of DCT coefficients. The proposed detection approach complies with the Moving Picture Experts Group (MPEG) and Motion Joint Photographic Experts Group (MJPEG) video compression standards, which improves the defect detection efficiency, in terms of space and computational cost by replacing the frame grabbers with MPEG encoders and by only considering I-frames in the MPEG or specific frames in the MJPEG sequence. We determine (1) the detection accuracy of using DCTb-Is with various compression rates, and (2) the lowest DCTb-I rates that allow defect detection. The DCTb-Is are tiled into blocks from which the energy and contrast features are calculated. The highest energy and lowest contrast blocks are detected and located in the textile as defects. The proposed approach is suitable for real-time detection because, unlike common defect-detection approaches, prior feature extraction from reference images is not required. Simulation results suggest that holes, stains and drop stitches can be detected and located from DCTb-Is if the compression rate is greater than 25%.

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“…Research in the digital image and computer vision field has yielded optical character recognition (OCR) for Chinese [5], Arabic [6], [7], Indian [8], and Latin characters [9], but no OCR system has yet been presented for Jawi characters. An OCR application framework requires four steps, namely: (1) preprocessing [10], [11], [12]; (2) segmentation; (3) feature extraction [13], [14]; and (4) pattern classiffiication [15]. At present, few algorithms for Jawi character recognition exist and these recognition algorithms have only been tested on a limited number of Jawi characters.…”

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confidence: 99%

Moment invariant-based features for Jawi character recognition

Arnia¹,

Saddami²,

Munadi³

2019

IJECE

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<p>Ancient manuscripts written in Malay-Arabic characters, which are known as "Jawi" characters, are mostly found in Malay world. Nowadays, many of the manuscripts have been digitalized. Unlike Roman letters, there is no optical character recognition (OCR) software for Jawi characters. This article proposes a new algorithm for Jawi character recognition based on Hu’s moment as an invariant feature that we call the tree root (TR) algorithm. The TR algorithm allows every Jawi character to have a unique combination of moment. Seven values of the Hu’s moment are calculated from all Jawi characters, which consist of 36 isolated, 27 initial, 27 middle, and 35 end characters; this makes a total of 125 characters. The TR algorithm was then applied to recognize these characters. To assess the TR algorithm, five characters that had been rotated to 90o and 180o and scaled with factors of 0.5 and 2 were used. Overall, the recognition rate of the TR algorithm was 90.4%; 113 out of 125 characters have a unique combination of moment values, while testing on rotated and scaled characters achieved 82.14% recognition rate. The proposed method showed a superior performance compared with the Support Vector Machine and Euclidian Distance as classifier.</p>

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Improvement of binarization performance by applying DCT as pre-processing procedure

Cited by 13 publications

References 14 publications

Effective and fast binarization method for combined degradation on ancient documents

Effective and fast binarization method for combined degradation on ancient documents

Real time textile defect detection using GLCM in DCT-based compressed images

Moment invariant-based features for Jawi character recognition

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