Bangla sorborno banjonborno4/9/2023 The basic symbols, obtained as the fundamental unit from segmentation process are recognized by neural classifier. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of documentĀ“s textual contents into paragraphs, lines, words and then at the level of basic symbols. Preprocessing, character segmentation, feature extraction and finally classification & recognitions are the major steps which are followed by a general OCR. Presence of touching characters in the scanned documents further increase the segmentation process thus creating a major problem while designing an effective character segmentation technique. One of the major reasons for the poor recognition rate is error in character segmentation. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN) which improves the efficiency. As there is no separation between the characters of the text written in Hindi similar to texts written in English, the Optical Character Recognition (OCR) systems developed for Hindi language carries a very poor recognition rate. "Hindi is the most spoken languages in India, with more than 300 million speaking it.
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