Automatic classification of deformed handwritten numeral characters
Luan Ling Lee, Natanael Gomes · 1999
Describes a method which utilizes Hopfield neural nets to classify those handwritten numerals presenting deformations and stylistic traces. Information for the classification consists of some topological image features and the image pixel distribution. If the recognition cannot be done by these features due to noise and deformations in the images of the numerals, the classification process is performed by four Hopfield neural nets. Using four such nets, we are able to minimize the problem caused by correlated patterns, and also to increase the neural classifier's pattern storage capacity. The proposed method was tested on 121 Brazilian bank checks, achieving a 92.4% correct recognition rate.