Performance improvement of dot-matrix character recognition by variation model based learning

Koji Endo, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

This paper describes an effective learning technique for optical dot-matrix characters recognition. Automatic reading system for dotmatrix character is promising for reduction of cost and labor required for quality control of products. Although dot-matrix characters are constructed by specific dot patterns, variation of character appearance due to three-dimensional rotation of printing surface, bleeding of ink and missing parts of character is not negligible. The appearance variation causes degradation of recognition accuracy. The authors propose a technique improving accuracy and robustness of dot-matrix character recognition against such variation, using variation model based learning. The variation model based learning generates training samples containing four types of appearance variation and trains a Modified Quadratic Discriminant Function (MQDF) classifier using generated samples. The effectiveness of the proposed learning technique is empirically evaluated with a dataset which contains 38 classes (2030 character samples) captured from actual products by standard digital cameras. The recognition accuracy has been improved from 78.37% to 98.52% by introducing the variation model based learning.

本文言語英語
ホスト出版物のタイトルComputer Vision - ACCV 2014 Workshops, Revised Selected Papers
編集者C.V. Jawahar, Shiguang Shan
出版社Springer Verlag
ページ147-156
ページ数10
ISBN(印刷版)9783319166308
DOI
出版ステータス出版済み - 1 1 2015
イベント12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, シンガポール
継続期間: 11 1 201411 5 2014

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9009
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他12th Asian Conference on Computer Vision, ACCV 2014
Countryシンガポール
CitySingapore
Period11/1/1411/5/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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