Text detection from scene images using sparse representation

A sparse representation based method is proposed for text detection from scene images. We start with edge information extracted using Canny operator and then group these edge points into connected components. Each connected component is labeled as text or non-text by a two-level labeling process: pixel level labeling and connected component labeling. The core of the labeling process is a sparsity test using an over-complete dictionary, which is learned from edge segments of isolated character images. Layout analysis is further applied to verify these text candidates. Experimental results show that improvements in both recall rate and detection accuracy in text detection have been achieved.