Camera based degraded text recognition using grayscale feature

Jun Sun, Yoshinobu Hotta, Yutaka Katsuyama, Satoshi Naoi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

As the rapid progress of digital imaging technology, camera based character recognition receives more and more attentions. One challenge in camera based OCR is the recognition for degraded text. Conventional OCR engines usually recognize on binary image. However, the performance drops dramatically as the degradation level increases. In this paper, a new recognition method is proposed to recognize degraded character based on dual eigenspace decomposition and synthetic degraded data. Then, the degraded character string is segmented by the combination of binary and grayscale analysis. Experiments on single character and text string recognition prove the effectiveness of our method.

Original languageEnglish
Title of host publicationProceedings of the Eighth International Conference on Document Analysis and Recognition
Pages182-186
Number of pages5
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event8th International Conference on Document Analysis and Recognition - Seoul, Korea, Republic of
Duration: 2005 Aug 312005 Sep 1

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2005
ISSN (Print)1520-5363

Conference

Conference8th International Conference on Document Analysis and Recognition
CountryKorea, Republic of
CitySeoul
Period05/8/3105/9/1

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Sun, J., Hotta, Y., Katsuyama, Y., & Naoi, S. (2005). Camera based degraded text recognition using grayscale feature. In Proceedings of the Eighth International Conference on Document Analysis and Recognition (pp. 182-186). [1575534] (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2005). https://doi.org/10.1109/ICDAR.2005.61