Text Detection in Images Based on Unsupervised Classification of High-Frequency Wavelet Coefficients

  • Authors:
  • Julinda Gllavata;Ralph Ewerth;Bernd Freisleben

  • Affiliations:
  • University of Siegen, Germany;University of Siegen, Germany;University of Siegen, Germany/ University of Marburg, Germany

  • Venue:
  • ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
  • Year:
  • 2004

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Abstract

Text localization and recognition in images is important for searching information in digital photo archives, video databases and web sites. However, since text is often printed against a complex background, it is often difficult to detect. In this paper, a robust text localization approach is presented, which can automatically detect horizontally aligned text with different sizes, fonts, colors and languages. First, a wavelet transform is applied to the image and the distribution of high-frequency wavelet coefficients is considered to statistically characterize text and non-text areas. Then, the k-means algorithm is used to classify text areas in the image. The detected text areas undergo a projection analysis in order to refine their localization. Finally, a binary segmented text image is generated, to be used as input to an OCR engine. The detection performance of our approach is demonstrated by presenting experimental results for a set of video frames taken from the MPEG-7 video test set.