Integrated segmentation and recognition of handwritten numeralswith cascade neural network

Authors:
Seong-Whan Lee;Sang-Yup Kim
Affiliations:
Dept. of Comput. Sci. & Eng., Korea Univ., Seoul;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Year:
1999

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Abstract

Proposes an integrated image segmentation and recognition method using a new type of cascade neural network that has been is developed to train the spatial dependencies in connected handwritten numerals. This network was originally extended from a multilayer feedforward neural network in order to improve its discrimination and generalization power. To verify the performance of the proposed method, recognition experiments with the National Institute of Standards and Technology (NIST) numerals databases have been performed. The experimental results reveal that the proposed method has a higher discrimination and generalization power than previous integrated segmentation and recognition methods have had. Moreover, the network size of the proposed method is smaller than that of the previous methods