A Learning-Based Text Detection Method in Camera Images


This paper proposed a learning-based text detection method in camera images. First, we find 280 pictures of book covers, CD covers and movie posters shot with cameras on Internet. We manually label and extract text regions in them. Second, based on statistical analysis of the difference between text and non-text samples, we get three sets of features which are used to produce weak classifiers. Third, Ada-boost is utilized to select and combine these weak classifiers into two-stage attentional cascade. At last, this two-stage cascade can detect text area in images by classifying sub-regions of images as text and non-text. Compared with previous works, this method is robust in detecting single characters, skewed and even vertical lines.

2011 IEEE International Conference on Computer Science and Automation Engineering
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.