Grayscale feature combination in recognition based segmentation for degraded text string recognition

Jun Sun, Yoshinobu Hotta, Katsuhito Fujimoto, Katsuyama Yutaka, Satoshi Naoi

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

Grayscale feature is very effective for degraded character recognition. While many papers focus on different feature extraction algorithms on single character recognition, few deals with the impact of the selected feature on segmentation. For recognition-based segmentation, a good recognition performance on single character may not always have good performance on segmentation. In this paper, two types of grayscale feature, the R-Feature and the S-Feature, are proposed based on dual-eigenspace decomposition. The RFeature is suitable for single character recognition. The SFeature is suitable for text string segmentation. These two feature are combined to further improve the performance for degraded Japanese text string recognition.

Original languageEnglish
Pages39-44
Number of pages6
Publication statusPublished - 2005
Event1st International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2005 - Seoul, Korea, Republic of
Duration: 2005 Aug 292005 Aug 29

Conference

Conference1st International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2005
CountryKorea, Republic of
CitySeoul
Period05/8/2905/8/29

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Sun, J., Hotta, Y., Fujimoto, K., Yutaka, K., & Naoi, S. (2005). Grayscale feature combination in recognition based segmentation for degraded text string recognition. 39-44. Paper presented at 1st International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2005, Seoul, Korea, Republic of.