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Bangla handwritten character recognition is one of the complex works because of the wide variation of the Bangla character. In this paper we proposed a new approach for extracting the features of Bangla handwritten characters and then recognition of those characters using artificial neural network has done. For the feature extraction process we have used a row and column basis segmentation process...
In Bangla alphabet, most of the characters share same features and such similarity misleads a recognizer as it makes decision based on measures of absolute difference. A preprocessing step called ‘scrambling’ and modification of existing grouping scheme are proposed to decrease the inter-character similarity among Bangla characters. The theory behind this proposal is originally inspired by experiments...
Recognition of text in natural scene images is becoming a prominent research area due to the widespread availablity of imaging devices in low-cost consumer products like mobile phones. To evaluate the performance of recent algorithms in detecting and recognizing text from complex images, the ICDAR 2011 Robust Reading Competition was organized. Challenge 2 of the competition dealt specifically with...
Segmentation and recognition of screen rendered text is a challenging task due to its low resolution (72 or 96 ppi) and use of antialiased rendering. This paper evaluates Hidden Markov Model (HMM) techniques for OCR of low resolution text -- both on screen rendered isolated characters and screen rendered text-lines -- and compares it with the performance of other commercial and open source OCR systems...
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