Recognition and Classification of Broken Characters using Feed Forward Neural Network to Enhance an OCR Solution
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 8
Abstract
The major problem faced by an Optical Character Recognizer (OCR) can be attributed to the presence of a large number of broken characters in a digital document image. Recognition of such documents accurately, that contain abundant broken characters still remains a challenge to most of the OCR solutions. Multi Layer Feed Forward Neural Network Classifier (MLFNC) can be used to enhance the efficiency of an OCR. MLFNC tries to improvise the recognition by classifying broken characters into a different group. This gives an opportunity to process broken characters in a more effective manner separately. Here, a new method has been proposed which uses feed forward neural network to classify broken characters prior any processing is done by an OCR with a considerable accuracy. MLFNC is a simple network with a very small time complexity due to which, there is a least effect on the time complexity of the solution provided by OCR.
Authors and Affiliations
Manas Yetirajam, , Manas Ranjan Nayak, , Subhagata Chattopadhyay
Keywords
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