Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten
محل انتشار: ماهنامه بین المللی مهندسی، دوره: 25، شماره: 2
سال انتشار: 1390
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 735
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شناسه ملی سند علمی:
JR_IJE-25-2_011
تاریخ نمایه سازی: 17 خرداد 1393
چکیده مقاله:
Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten character recognition. This paper identifies the most suitable NN for the design of hand written English character recognition system. Different Neural Network (NN) topologies namely, back propagation neural network, nearest neighbour network and radial basis function network are built to classify the characters. All the NN based Recognition systems use the same training data set and are trained for the same target mean square error. Two hundred different character data sets for each of the 26 English characters are used to train the networks. The performance of the recognition systems is compared extensively using test data to draw the major conclusions of this paper
کلیدواژه ها:
Handwritten Character Recognition ، Image Processing ، Feature Extraction ، Back Propagation Neural Network ، Nearest Neighbour Network ، Radial Basis Function Network
نویسندگان
j Pradeep
Department of Electronics & Communication Engineering, Pondicherry Engineering College, Puducherry, India
e Srinivasan
Department of Electronics & Communication Engineering, Pondicherry Engineering College, Puducherry, India
s Himavathi
Department of Electrical & Electronics Engineering, Pondicherry Engineering College, Pondicherry, India