Text detection in images based on unsupervised classification of high-frequency wavelet coefficients

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.