Tamil Palm Leaf Manuscript Character Segmentation using GLCM feature extraction
Keywords:
Gaussian, Bilateral, GLCM, PSNR, SSIM, MSE, Homogeneity, Angular Second Moment (ASM).Abstract
The main objective of this proposed effort is to advance the system that empowers recognition of Tamil characters from palm leaf and inscription through captured images and stock them for forthcoming use. Some training mechanism has done with several methodologies, but distinguishing Tamil characters stances challengeable mission. Tamil language is considered too complex compared to any other language because of the presences of curved, slope, twist, pits and it will vary writing style of individual to individual. More research needs adapting ancient Tamil characters to modern Tamil characters to extend the aim of creating computerized system for providing improved understanding of human knowledge. This proposed work is applicable for segmenting Tamil characters and store it in an organized system folder for further processing of the image. Gray-Level Co-occurrence Matrix (GLCM) feature extraction is used to quantify the statistical features of segmented characters. At this juncture segmented Tamil Characters are compared with Palm leaf manuscript, Stone Inscription, Handwritten characters and document characters using GLCM feature and the results are promising
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