Image Denoising
2016-08-23
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An off-line handwritten alphabetical character recognition system using multilayer feed forward neural
network is described in the paper. A new method, called, diagonal based feature extraction is introduced
for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets
written by various people, are used for training the neural network and 570 different handwritten
alphabetical characters are used for testing. The proposed recognition system performs quite well
yielding higher levels of recognition accuracy compared to the systems employing the conventional
horizontal and vertical methods of feature extraction. This system will be suitable for converting
handwritten documents into structural text form and recognizing handwritten names Handwriting recognition has been one of the most fascinating and challenging research areas in
field of image processing and pattern recognition in the recent years [1
network is described in the paper. A new method, called, diagonal based feature extraction is introduced
for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets
written by various people, are used for training the neural network and 570 different handwritten
alphabetical characters are used for testing. The proposed recognition system performs quite well
yielding higher levels of recognition accuracy compared to the systems employing the conventional
horizontal and vertical methods of feature extraction. This system will be suitable for converting
handwritten documents into structural text form and recognizing handwritten names Handwriting recognition has been one of the most fascinating and challenging research areas in
field of image processing and pattern recognition in the recent years [1
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