ঝ This is produced using the naval sounds while the tongue touches the upper part of mouth cavity The better you pronounce a letter in a word, the more understood you will be in speaking the Bengali language.īelow is a table showing the Bengali alphabet and how it is pronounced in English, and finally examples of how those letters would sound if you place them in a word. Without it, you will not be able to say words properly even if you know how to write those words. A performance of approximately 90% correct recognition rate is achieved.Learning the Bengali alphabet is very important because its structure is used in every day conversation. The classifier is trained and tested for printed Hindi texts. For developing the neural classifier, back-propagation neural network with two hidden layer is used. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. In this work, three feature extraction techniques: histogram of projection based on mean distance, histogram of projection based on pixel value and vertical zero crossing has been used to improve the rate of recognition. 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 document´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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