High accuracy recognition of etl9b using exclusive learning neural network - II (ELNET - II)

Kazuki Saruta, Nei Kato, Masato Abe, Yoshiaki Nemoto

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

In earlier works we proposed the Exclusive Learning neural NET work (ELNET), which can be utilized to construct large scale recognition system for Chinese characters. However, this did not resolve the problem of how to use training samples effectively to generate more suitable recognition boundaries. In this paper, we propose ELNET-II wherein an attempt has been made to deal with this problem. In comparison with ELNET, selection method of training samples is improved. And the number of module size are variable according to the number of training samples for each module. In recognition experiment for ETL9B (3036 categories) using ELNET-II, we obtained a recognition rate of 95. 84% as maximum recognition rate. This is the first time that such a high recognition rate has been obtained by neural networks.

Original languageEnglish
Pages (from-to)516-522
Number of pages7
JournalIEICE Transactions on Information and Systems
VolumeE79-D
Issue number5
Publication statusPublished - 1996 Jan 1
Externally publishedYes

Keywords

  • ELNET-II
  • ETL9B
  • Handwritten character recognition
  • Neural networks

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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