Thinning of handwritten Gujarati numerals
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skeletonization "also known as Thinning" is one of the crucial approach of featureextraction and recognition task.Thinning is a most significant pre-processingtechnique in many image processing applications such as Character recognition,shape analysis and computer vision. Thinning is the process to find single widthpixel line from the multi-width pixel which preserve the lines,curves and arcs.Thinningmakes recognition task easy and more efficient due to the fact that thinned imageof the character is less complex than the original character. Hence, OCR systemis most reliant on the performance of the thinning algorithm.This thesis proposesmedial axis based thinning algorithm to obtain skeleton from the original character.A two-phase is used to obtain the final skeleton image. Initially, we generateprimary skeleton image using the medial axis based approach in the first phase.In the second phase, we use Auto-encoder neural network to obtain the betterskeleton. The proposed thinning algorithm help to preserve the shape of the characterand also ensure unit width. The experiment is conducted on handwrittenGujarati numerals characters. we use a large set of performance measurement parameterfor performance evaluation. we achieve a better result than other existingthinning algorithms.
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