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Fast and Accurate Tree-Based Clustering for Japanese/Chinese Character Recognition

  • Yuichi Abe
  • Takahiro Sasaki
  • Hideaki Goto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

Recognizing text in natural scene images is very important to develop various systems such as an assistant device for visually-impaired people. Multilingual scene text recognition is also becoming important for wearable camera devices with language translation feature. Since computational resources are limited on such mobile devices, fast and accurate Optical Character Recognition (OCR) algorithm is needed. Nearest Neighbor (NN) search is quite popular in feature vector-based OCR systems, and its speed improvement is required. In this paper, we develop an OCR scheme with tree-based clustering technique with LDA (Linear Discriminant Analysis) aiming at real-time Japanese/Chinese character recognition. The experimental results using ETL9B dataset show that our proposed method is 94.6% faster than our previous method, also beating other techniques, at mere 0.24% accuracy drop from the full linear search.

Keywords

Fast Nearest Neighbor search Linear Discriminant Analysis (LDA) real-time character recognition Approximate Nearest Neighbor (ANN) search multilingual OCR 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yuichi Abe
    • 1
  • Takahiro Sasaki
    • 1
  • Hideaki Goto
    • 2
  1. 1.Graduate School of Information SciencesTohoku UniversityJapan
  2. 2.Cyberscience CenterTohoku UniversityJapan

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