Automated optical recognition of degraded handwritten characters
Emade Darwiche, Abhijit S. Pandya, Anil D. Mandalia · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
This paper reports on a new approach in the field of automated optical recognition of handwritten characters. The approach combines geometrical and topological features, distribution of points, and Alopex based neural network to achieve a high recognition rate. A considerable enhancement in speed is achieved by implementing the process on a compressed image. Distortion tolerant features along with noise removal and region merging permit the handling of degraded documents and characters. Software implementation of the system experimented on the NIST database yields to a recognition rate of 92.4 for numerals and upper-case letters.