NDLI: Character recognition by neural networks with single-layer training and rejection mechanism

Content Provider	IEEE Xplore Digital Library
Author	Joonho Lim Eelwan Lee Soo-Ik Chae
Copyright Year	1994
Description	Author affiliation: Dept. of Electron. Eng., Seoul Nat. Univ., South Korea (Joonho Lim; Eelwan Lee; Soo-Ik Chae)
Abstract	For many real applications of pattern classification problems, it is more important to reduce the misclassification rate than to increase the rate of successful classification. In this paper, we propose a single-layer neural network with two rejection mechanisms for character recognition problems, which guarantees a very low misclassification rate. The proposed architecture is a cascaded connection of an SLP network and a simple combinational circuit. Comparing to the MLP network, it yields fast learning and requires a simple hardware architecture. We also introduce a new linearly separable coding scheme for training the SLP network to reduce the misclassification rate. We prepared two databases: one with 135,000 digit patterns and the other with 117,000 letter patterns. Then we applied the proposed method to the classification problem for the databases and results show that the misclassification rate is significantly low with maintaining a high recognition rate.<>
Starting Page	327
Ending Page	330
File Size	375850
Page Count	4
File Format	PDF
ISBN	078031915X
DOI	10.1109/ISCAS.1994.409592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-05-30
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Character recognition Neural networks Noise figure Pattern classification Databases Hamming distance Combinational circuits Neural network hardware Nonhomogeneous media Backpropagation algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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