Katakana character recognition using neural networks

Date

1993-05

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

This thesis presents a new preprocess1ng model for character recognition and simulation experiments. The goal of the model is to recognize Japanese Katakana characters by dealing with segments having vectors with 9 blocks. The JK9BVL preprocessor deals with a segment of a character as a vector and presents the position, length, and direction of the vector with the output value of each block which consists of 9 blocks. The simulation experiments consist of two parts. The first part involves learning a character set and testing two noise data sets. The second part involves the recognition test of two sets of unknown character sets.

Description

Rights

Availability

Unrestricted.

Keywords

Optical pattern recognition, Optical data processing, Neural networks (Computer science), Japanese language -- Alphabet -- Data processing

Citation