Fast and accurate candidate reduction using the multiclass LDA for Japanese/Chinese character recognition

Ryosuke Odate, Hideaki Goto

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Acceleration of Optical Character Recognition (OCR) algorithms is quite important for developing real-time applications on mobile devices with limited computational performances. Multilingual scene text recognition is becoming more important for mobile and wearable devices. Since Japanese and Chinese have thousands of characters, a fast and accurate character recognition algorithm is required. We developed and proposed a tree-based clustering technique combined with Linear Discriminant Analysis (LDA), and it worked fine with ETL9B dataset consisting of Japanese handwritten characters. However, a significant performance degradation with HCL2000 Chinese handwritten character dataset was found. In this paper, we formalize the candidate reduction technique for the Nearest Neighbor (NN) problems, and propose an improved method that works fine with both Japanese and Chinese character sets. Experimental results show that our method is faster and more accurate than the existing acceleration techniques such as Approximate Nearest Neighbor (ANN) search and Locality Sensitive Hashing (LSH).

本文言語English
ホスト出版物のタイトル2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
出版社IEEE Computer Society
ページ951-955
ページ数5
ISBN(電子版)9781479983391
DOI
出版ステータスPublished - 2015 12 9
イベントIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
継続期間: 2015 9 272015 9 30

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2015-December
ISSN(印刷版)1522-4880

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
国/地域Canada
CityQuebec City
Period15/9/2715/9/30

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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