NDLI: Performance analysis of hybrid feature extraction technique for recognizing English handwritten characters

Content Provider	IEEE Xplore Digital Library
Author	Pradeep, J. Srinivasan, E. Himavathi, S.
Copyright Year	2012
Description	Author affiliation: Department of ECE, Pondicherry Engineering College, Pondicherry, India (Pradeep, J.; Srinivasan, E.) \|\| Department of EEE, Pondicherry Engineering College, Pondicherry, India (Himavathi, S.)
Abstract	In this paper, an off-line handwritten English character recognition system using hybrid feature extraction technique and neural network classifiers are proposed. A hybrid feature extraction method combines the diagonal and directional based features. The proposed system suitably combines the salient features of the handwritten characters to enhance the recognition accuracy. Neural Network (NN) topologies, namely, back propagation neural network and radial basis function network are built to classify the characters. The k-nearest neighbour network is also built for comparison. The Feed forward NN topology exhibits the highest recognition accuracy and is identified to be the most suitable classifier. The proposed system will aid applications for postal/parcel address recognition and conversion of any hand written document into structural text form. The performance of the recognition systems is compared extensively using test data to draw the major conclusions of this paper.
Starting Page	373
Ending Page	377
File Size	414151
Page Count	5
File Format	PDF
ISBN	9781467348065
e-ISBN	9781467348058
DOI	10.1109/WICT.2012.6409105
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-30
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handwriting recognition Image segmentation Accuracy Image processing Handwritten Character Recognition Artificial neural networks Nearest Neighbour Network Feature extraction Radial Basis function Network Feed forward propagation Neural Network Character recognition Feeds
Content Type	Text
Resource Type	Article

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