AN INTEGRATED ARCHITECTURE FOR RECOGNITION OF TOTALLY UNCONSTRAINED HANDWRITTEN NUMERALS
Amar Deep Gupta, M.V. Nagendraprasad, A. LIU, P.S.P. WANG, S. AYYADURAI · International Journal of Pattern Recognition and Artificial Intelligence · 1993
A multi-staged system for off-line handwritten numeral recognition is presented here. After scanning, the digitized binary bitmap image of the source document is passed through a preprocessing stage which performs segmentation, thinning and rethickening, normalization, and slant correction. The recognizer is a three-layered neural net trained with back-propagation algorithm. While a few systems that use three-layered nets for recognition have been presented in the literature, the contribution of our system is based on two aspects: elaborate preprocessing based on structural pattern recognition methods combined with a neural net based recognizer; and integration of neural net based and structural pattern recognition methods to produce high accuracies.