Natural Scene Text Detection with Multi-channel Connected Component Segmentation

Text detection attracts more and more attention these years. But natural scene text detection is still a challenge problem due to the variations of text and the complexity of the background. In this paper an efficient text detection method with multi-channel connected component segmentation is proposed. First, connected component segmentation is done using Markov Random Field with local contrasts, colors and gradients of RGB channels. Three segmentation images are obtained corresponding to the three channels. Then, non-text connected components in the three segmentation images are removed. Finally, the remaining text components in the three segmentation images are merged and then grouped into words. Experiments on the ICDAR 2003 dataset and the ICDAR2011 dataset demonstrate that this method compares favorably with the state-of-the-art methods.