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.