A Hybrid Approach to Detect and Localize Texts in Natural Scene Images

Yi-Feng Pan, Xinwen Hou, Cheng-Lin Liu
2011 IEEE Transactions on Image Processing  
Text detection and localization in natural scene images is important for content-based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, the variations of text font, size and line orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help segment candidate
more » ... text components by local binarization. To efficiently filter out the non-text components, a conditional random field (CRF) model considering unary component properties and binary contextual component relationships with supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring manual tuning. Experimental results evaluated on the ICDAR 2005 competition dataset show that our approach yields higher precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image dataset with promising results. Index Terms-Conditional random field (CRF), connected component analysis (CCA), text detection, text localization.
doi:10.1109/tip.2010.2070803 pmid:20813645 fatcat:jom6t3r67bam7hp5ctx4hppbwu