7 March 1996 Integrated segmentation and recognition of connected handwritten characters with recurrent neural network
Author Affiliations +
Abstract
In this paper, we propose an efficient method for integrated segmentation and recognition of connected handwritten characters with recurrent neural network. In the proposed method, a new type of recurrent neural network is developed for training the spatial dependencies in connected handwritten characters. This recurrent neural network differs from Jordan's and Elman's recurrent networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving the discrimination and generalization power. In order to verify the performance of the proposed method, experiments with the NIST database have been performed and the performance of the proposed method has been compared with those of the previous integrated segmentation and recognition methods. The experimental results reveal that the proposed method is superior to the previous integrated segmentation and recognition methods in view of discrimination and generalization ability.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Seong-Whan Lee, Eung-Jae Lee, "Integrated segmentation and recognition of connected handwritten characters with recurrent neural network", Proc. SPIE 2660, Document Recognition III, (7 March 1996); doi: 10.1117/12.234707; https://doi.org/10.1117/12.234707
PROCEEDINGS
11 PAGES


SHARE
Back to Top