Context-based text detection in natural scenes

• Published in: 2012 19th IEEE International Conference on Image Processing pp. 1857 - 1860
• Main Authors: Yuning Du, Genquan Duan, Haizhou Ai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2012
• Subjects: Color; Connected Component Analysis; Context; Context of Text; Detectors; Image color analysis; Robustness; Support vector machines; Text Detection; Training
• ISBN: 1467325341; 9781467325349
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2012.6467245

Text detection in natural scenes is fundamental for text image analysis. In this paper, we propose a context-based approach for robust and fast text detection. Our main contribution is that we introduce a new concept of key region, which is described with context according to stroke properties, appearance consistency and specific spatial distribution of text line. With such context descriptors, we adopt SVM to learn a context-based classifier to find key regions in candidate regions. Therein, candidate regions are connected components generated by local binarization algorithm in the areas, which are detected by an offline learned text patch detector. Experimental results on two benchmark datasets demonstrate that our approach has achieved competitive performances compared with the state-of-the-art algorithms including the stroke width transform (SWT) [1] and the hybrid approach based on CRFs [2] with speedup rates of about 1.7x~4.4x.