Text detection in born-digital images using IT-LBP

Chao Zeng, Wenjing Jia, Xiangjian He, Liming Zhang

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)


Fine text detection plays a crucial role in a text detection algorithm as it is capable of removing the false alarms while keeping the detected text lines in coarse text detection. Good performance of a machine learning-based fine text detection heavily depends on the powerful feature to depict the characteristics of text. In this paper, a novel texture-based descriptor, named IT-LBP, is proposed by considering horizontal, vertical, diagonal and anti-diagonal directions of character strokes to better describe the texture of text. The new feature demonstrates its superiority by comparing with other texture-based features. The new feature is used to train an SVM classifier to further filter out non-text candidates. The ICDAR 2011 born-digital image dataset is used to evaluate and demonstrate the performance of the proposed method. Following the same performance evaluation criteria, the proposed method outperforms the winner algorithm of the ICDAR 2011 Robust Reading Competition Challenge 1.

Original languageEnglish
Pages (from-to)127-142
Number of pages16
JournalJournal of Algorithms and Computational Technology
Issue number1
Publication statusPublished - 1 Mar 2014
Externally publishedYes


  • IT-LBP
  • Multiple layer image
  • maximum gradient difference
  • text detection

ASJC Scopus subject areas

  • Numerical Analysis
  • Computational Mathematics
  • Applied Mathematics


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