<title>Recognition of digits using spatiotemporal neural networks</title>

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%.