Devnagari Text To Speech Conversion
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A good text recognizer has many commercial and practical applications, e.g. from searching data in scanned book to automation of any organization, like post office, which involve manual task of interpreting text. The problem of text recognition has been attempted by many different approaches. In Feature extraction approach, statistical distribution of points is analyzed and orthogonal properties extracted. For each symbol a feature vector is calculated and stored in database. And recognition is done by finding distance of feature vector of input image to that of stored in the database, and out...
A good text recognizer has many commercial and practical applications, e.g. from searching data in scanned book to automation of any organization, like post office, which involve manual task of interpreting text. The problem of text recognition has been attempted by many different approaches. In Feature extraction approach, statistical distribution of points is analyzed and orthogonal properties extracted. For each symbol a feature vector is calculated and stored in database. And recognition is done by finding distance of feature vector of input image to that of stored in the database, and outputting the symbol with minimum deviation. Though this technique gives lot better results on handwritten characters, but is very sensitive to noise and edge thickness. Template matching is one of the simplest approaches. In this many templates of each word are maintained for a input image, error or difference with each template is computed. The symbol corresponding to minimum error is output.
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