95 articles 

inria-00104438, version 1

A Compact On-line and Off-line Combined Recognizer

Hideto Oda () 1, Bilan Zhu () 1, Junko Tokuno () 1, Motoki Onuma () 1, Akihito Kitadai () 1, Masaki Nakagawa () 1

Tenth International Workshop on Frontiers in Handwriting Recognition (2006)

Abstract: This paper describes a compact on-line/off-line combined handwriting recognizer for Japanese characters. Conventional combined recognizers mainly consider the recognition accuracy, though recognition speed and memory size are important as well. Especially, the off-line method requires a large prototype dictionary, and therefore the on-line/off-line combined recognizers are difficult to use practically in a small computer. In order to tackle this problem, we propose an on-line/offline combined recognizer where an off-line recognizer is composed of the Modified Quadratic Discriminant Function whose dictionary size is significantly reduced. Moreover, its on-line recognizer is composed of a structured character pattern representation (SCPR) dictionary which reduces the total size of memory and Linear-time Elastic Matching (LTM) which reduces the computation time. Experimental results show that the cumulative recognition rate of top 5 candidates of a proposed 1MB dictionary is 97.3% (almost the same as that of a conventional 90MB dictionary) and that the recognition speed of the 1MB dictionary is 1.75 times faster then that of the 90MB dictionary.

  • 1:  Tokyo University of Agriculture and Technoloy
  • Tokyo University of Agriculture and Technology
  • Domain : Computer Science/Document and Text Processing
    Computer Science/Computer Vision and Pattern Recognition
  • Keywords : Handwritten character recognition – on-line recognition – off-line recognition – multiple classifier systems – evaluation score normalization
  • Comment : http://www.suvisoft.com
 
  • inria-00104438, version 1
  • oai:hal.inria.fr:inria-00104438
  • From: 
  • Submitted on: Friday, 6 October 2006 15:29:02
  • Updated on: Friday, 6 October 2006 15:33:06