Spectral Analysis of Projection Histogram for Enhancing Close matching character Recognition in Malayalam

Divakaran, Sajilal

doi:10.5121/ijcsit.2012.4212

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…There are some misclassifications because of close-matching characters. For improvement in the existing OCR system a specialized pattern classifier for close-matching characters of Malayalam has been developed [2]. In this, projection histogram combined with Discrete Fourier Transform (DFT) coefficients of closematching characters are taken as the new feature vector and used for better classification [5].…”

Section: Optical Character Recognition (Ocr)mentioning

confidence: 99%

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Malayalam Character Recognition using Singular Value Decomposition

Anil¹,

Pradeep²,

Midhun³

et al. 2014

IJCA

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This paper provides a classification methodology of Malayalam characters segmented from scanned document images. Optical Character Recognition (OCR) is one of the successful area which has wide variety of applications related to pattern recognition. This paper describes segmented character recognition using Singular Value Decomposition (SVD). Euclidean distance measure is used for finding the nearest character class of the segmented character image during testing. For each character class, a resultant template is created from training character images using the proposed approach, which in turn reduces the comparisons required for classification. The result obtained from the experiment shows that this method provides an accuracy of 97%.

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Section: Optical Character Recognition (Ocr)mentioning

confidence: 99%

“…To reduce the number of comparisons mean of 50 different fonts of each character is calculated. A sample image is shown in figure (1) and figure (2) shows how the same image is stored after taking mean [10] [11].…”

mentioning

confidence: 99%

Malayalam Character Recognition using Singular Value Decomposition

Anil¹,

Pradeep²,

Midhun³

et al. 2014

IJCA

View full text Add to dashboard Cite

show abstract

“…The integration of cameras in portable devices, especially in mobile phones and PDAs, has increased the need for image processing software which can work fast, use as little memory as possible and produce good quality images. Having this scenario in mind, many algorithms have been produced in order to enhance the images obtained by these devices, including [1,2,3,4,5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Fast Hue-Preserving Histogram Equalization Methods For Color Image Contrast Enhancement

Menotti

Najman

Facon

et al. 2012

IJCSIT

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In this work, we formalize a generic fast hue-preserving histogram equalization method based on the RGB color space for image contrast enhancement and two versions of that generic process. The first method estimates a RGB 3D histogram to be equalized using R-red, G-green, and B-blue 1D histograms, while the second method employs RG, RB, and GB 2D histograms. The histogram equalization is performed using shift hue-preserving transformations, avoiding unrealistic colors. Our methods have linear time and space complexities with respect to the size of the image and do not need to apply conversions from a color space to another in order to perform the image enhancement. Such design complies with real-time applications requirements. An objective assessment comparing our methods and others is performed using a contrast measure and a color image quality measure, where the quality is established as a weighting of the naturalness and colorfulness indexes. We analyze 300 images from the dataset of the University of Berkeley. Experiments show that the value of the image contrast produced by our methods is in average 50% greater than the original image value, keeping the quality of the produced images close to the original one.

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