Digital image processing is the usage of computer algorithms for the analysis and manipulation of images. This work emphasis local thresholding technique for the segmentation of characters in palm leaf manuscript images. The preprocessing stage comprises of filtering and image enhancement. The filtering of noise was done by decision based median filter and contrast local adaptive histogram equalization was applied for enhancement. For segmentation, Otsu global thresholding and local thresholding techniques like Niblack, Sauvola and Bernsen algorithms were evaluated. The Sauvola local thresholding generates more efficient results than the global thresholding and other local thresholding techniques. The computational complexity of Sauvola thresholding is considerably low and the performance of thresholding techniques was evaluated by entropy measure. The Sauvola thresholding resultant image has low entropy value when compared with other thresholding techniques. The algorithms were developed in Matlab 2010a and evaluated on the real-time images acquired by canon SX600HS camera.

Palm leaf manuscript; Decision-based median filter; CLAHE; thresholding; Shannon entropy

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