Title :
Farsi handwritten character recognition with moment invariants
Author :
Dehghan, Mehdi ; Faez, Karim
Author_Institution :
Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran
Abstract :
This paper introduces an experimental evaluation of the effectiveness of utilizing various moments as pattern features in recognition of the handwritten Farsi characters. The moments that have been used are Zernike moments, pseudo Zernike moments, and Legendre moments. We have used an unsupervised neural network (ART2) for this application, so that the clusters are formed only based on inherent properties of pattern features. The performance of classification is dependent on the moment order as well as the type of the moment invariant, but the classification error rate was below 10% in all cases. The pseudo Zernike moments of order 5 had the best performance among all the moment invariants. Its error rate and discrimination factor were 3.06% and 96.92% respectively
Keywords :
ART neural nets; handwriting recognition; pattern classification; Farsi handwritten character recognition; Legendre moments; Zernike moments; classification error rate; classification performance; clusters; discrimination factor; error rate; inherent properties; moment invariant type; moment invariants; moment order; pattern features; pseudo Zernike moments; unsupervised neural network; Adaptive systems; Character recognition; Error analysis; Handwriting recognition; Neural networks; Pattern recognition; Polynomials; Resonance; Shape; Testing;
Conference_Titel :
Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on
Conference_Location :
Santorini
Print_ISBN :
0-7803-4137-6
DOI :
10.1109/ICDSP.1997.628387
