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Proceedings Paper

Recognition of digits using spatiotemporal neural networks
Author(s): Chong Sik Lee; Jae Ho Chung
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Paper Abstract

In this paper, a new approach for Korean digit recognition using the Spatio-Temporal Neural Network (STNN) is reported. Two approaches are proposed, and the digit recognition rate of 95% is achieved. In the first approach, the LPC-cepstrums are used as STNN's input patterns. The LPC-cepstrums are derived from the linear predictive coding (LPC) coefficients that computed through the vocal tract analysis. The recognition rate of 90% is achieved, which is higher than the performance rate of 83.5% that is achieved by STNN with LPC coefficients as the input patterns. Using the LPC-cepstrums as the input patterns, in the second approach, when the difference between the highest two scores of ten STNNs' outputs is less than the predefined threshold value, the distortions of the two digit candidates from the input signal are computed using the Euclidean cepstral distance measure. Comparing the two distortions we then determine which STNN between the two produces smaller distortion, and the corresponding digit is declared as the recognized final digit. This simple added feature improves the performance of the STNN significantly from 90% to 95%.

Paper Details

Date Published: 6 April 1995
PDF: 11 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205204
Show Author Affiliations
Chong Sik Lee, Inha Univ. (South Korea)
Jae Ho Chung, Inha Univ. (South Korea)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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