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Proceedings ArticleDOI

Enrichment of epigraphy using image processing

TL;DR: The proposed work describes the difficulties during the conversion of inscription digitization, preservation and trifling dissimilarities among forefront and background and proposes methodology that enhances the words and recognizes the characters alone.
Abstract: The proposed work describes the difficulties during the conversion of inscription digitization, preservation and trifling dissimilarities among forefront and background. Basically the inscriptions were neither retained traditional size and nor the shape. Even though they doesn't have colour discrepancy linking foreground and background. In priviling technique describes the extractions in the inscription by using NGFICA method. Our method enhances the words and recognizes the characters alone. In proposing methodology the inscription that has been enhanced, recognized effortlessly.
Citations
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Proceedings ArticleDOI
01 Feb 2018
TL;DR: This model consists phase congruency and of Gaussian model based background elimination using expectation maximization(EM) algorithm, preprocessing and binarization, which removes the background noise completely where foreground characters are untouched.
Abstract: Epigraphs are important sources for reshaping our culture and history. They have a remarkable importance to mankind. But modern epigraphists find it difficult to interpret the information in scripts. It is mainly because inscriptions are eroded over a period of time due to natural calamities. Scripts of ancient times are largely unknown. Character sets used have changed from one form to another over the centuries. Therefore, for reading ancient scripts the characters have to be extracted. In this paper, a model for enhancement and binarization of historical epigraphs is proposed. This model consists phase congruency and of Gaussian model based background elimination using expectation maximization(EM) algorithm, preprocessing and binarization. In binarization, phase based features are used with specialised filters. Adaptive Gaussian filters are used to smoothen the output images. Weighted mean angle is calculated to differentiate the foreground from the background. EM algorithm removes the background noise completely where foreground characters are untouched. Proposed method is tested on different datasets of inscriptions and epigraphs. Obtained results are compared with the existing classical algorithms.

4 citations


Cites background from "Enrichment of epigraphy using image..."

  • ...R Manoorubini in [7] explains difficulties during the conversion of inscription digitization and preservation....

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References
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Journal ArticleDOI
TL;DR: This paper offers the researchers a link to public image database for the algorithm assessment of text extraction from natural scene images and draws attention to studies on the first two steps in the extraction process, since OCR is a well-studied area where powerful algorithms already exist.

149 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed identification technique is accurate, easy for extension, and tolerant to noise and various types of document degradation.
Abstract: This paper reports an identification technique that detects scripts and languages of noisy and degraded document images. In the proposed technique, scripts and languages are identified through the document vectorization, which converts each document image into a document vector that characterizes the shape and frequency of the contained character or word images. Document images are vectorized by using vertical component cuts and character extremum points, which are both tolerant to the variation in text fonts and styles, noise, and various types of document degradation. For each script or language under study, a script or language template is first constructed through a training process. Scripts and languages of document images are then determined according to the distances between converted document vectors and the preconstructed script and language templates. Experimental results show that the proposed technique is accurate, easy for extension, and tolerant to noise and various types of document degradation.

72 citations

Journal ArticleDOI
TL;DR: A contrast enhancement approach utilizing a new type of mathematical morphology called rotational morphological processing is introduced, which is quantitatively evaluated and then applied to some medical images.
Abstract: Image processing methods significantly contribute to visualization of images captured by biomedical modalities (such as mammography, X-ray computed tomography, magnetic resonance imaging, and light and electron microscopy). Quantitative interpretation of the deluge of complicated biomedical images, however, poses many research challenges, one of which is to enhance structural features that are scarcely perceptible to the human eye. This study introduces a contrast enhancement approach based on a new type of mathematical morphology called rotational morphological processing. The proposed method is applied to medical images for the enhancement of structural features. The effectiveness of the method is evaluated quantitatively by the contrast improvement ratio (CIR). The CIR of the proposed method is 12.1, versus 4.7 and 0.1 for two conventional contrast enhancement methods, clearly indicating the high contrasting capability of the method.

39 citations

Journal ArticleDOI
TL;DR: This letter deals with the color image edge enhancement issue using clustering ideas and based on the use of potential functions (Parzen windows) and employing the mountain clustering method and modifying it appropriately.
Abstract: This letter deals with the color image edge enhancement issue using clustering ideas and based on the use of potential functions (Parzen windows). Two algorithms are proposed. The first uses potential functions (PF's) and selects the output as the vector maximizing the PF. The second one elaborates further by employing the mountain clustering method and modifying it appropriately. Both algorithms are robust in the presence of noise, Gaussian and impulsive.

26 citations

Proceedings ArticleDOI
28 Mar 2013
TL;DR: The proposed method improves word and character recognition accuracies of the OCR system by 65.3% and 54.3%, respectively, and is a suitable method for separating signals from a mixture of highly correlated signals.
Abstract: This paper addresses the problems encountered during digitization and preservation of inscriptions such as perspective distortion and minimal distinction between foreground and background. In general inscriptions neither possess standard size and shape nor colour difference between the foreground and background. Hence the existing methods like variance based extraction and Fast-ICA based analysis fail to extract text from these inscription images. Natural gradient Flexible ICA (NGFICA) is a suitable method for separating signals from a mixture of highly correlated signals, as it minimizes the dependency among the signals by considering the slope of the signal at each point. We propose an NGFICA based enhancement of inscription images. The proposed method improves word and character recognition accuracies of the OCR system by 65.3% (from 10.1% to 75.4%) and 54.3% (from 32.4% to 86.7%) respectively.

8 citations