Connected character recognition with a neural network

Author

Fukushima, Kunihiko

Author_Institution

Dept. of Biophys. Eng., Osaka Univ., Japan

fYear

1993

fDate

20-22 Oct 1993

Firstpage

240

Lastpage

243

Abstract

The selective attention model proposed by the author is a neural network model which has the ability to segment patterns, as well as the function of recognizing them. The principles of this selective attention model have been extended for the recognition and segmentation of connected characters. The topics discussed include the network architecture, pattern recognition, segmentation, repairing imperfect patterns, attention focusing, search control, attention switching, size and position information, and computer simulation. Improvement of the system using bend detectors is discussed

Keywords

character recognition; digital simulation; image segmentation; neural nets; attention focusing; attention switching; bend detectors; computer simulation; connected character recognition; imperfect pattern repair; network architecture; neural network model; pattern recognition; pattern segmentation; position information; search control; selective attention model; size information; Character recognition; Deformable models; Feature extraction; Handwriting recognition; Image segmentation; Neural networks; Optical wavelength conversion; Pattern matching; Pattern recognition; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on

Conference_Location

Tsukuba Science City

Print_ISBN

0-8186-4960-7

Type

conf

DOI

10.1109/ICDAR.1993.395740

Filename

395740