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Statistics report
Aug. 2019
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 77
Paper Published : 1176
No. of Authors : 2946
  Journal Paper

Paper Title
Deep Learning Approach for Offline Handwritten Character Recognition

Abstract
Character recognition is one of the main aspects of computer vision. It is a technology that provides full alphanumeric representation of printed and handwritten characters. Many researchers have proposed various methods and approaches for character recognition. Neural network method performs well for pattern classification and is utilized both for feature extraction and classification. This paper aims to build an offline handwritten character recognition (HWCR) using deep neural network (DNN). EMNIST Letters and Balanced dataset are used in training and testing phase for character classification. The proposed deep neural network uses three hidden layers and a softmax layer. The performance of DNN is derived from the total no of characters correctly detected. The performance of DNN is compared with logistic regression and shallow network method. Keywords - Deep Neural Network, EMNIST, Computer Vision, Handwritten Character Recognition (HWCR).


Author - Shamlesh U. Dube, Ameya N. Naik

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| Published on 2018-12-22
   
   
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