Multi-orientation scene text detection with multi-information fusion
Wei-Yi Pei, Chun Yang, Lih‐Jen Kau, Xu-Cheng Yin · 2016
We construct a robust and precise multi-orientation text detection system in scene images which can extensively locate possible characters with multi-information fusion. In our method, an adaptive multi-channel character grouping algorithm is first proposed to extract all possible character candidates robustly, and an AdaBoost classifier is then to properly identify character candidates as characters or non-characters. A single-link clustering with distance metric learning is thereafter used to adaptively group characters into text regions, and an effective hybrid filter with Convolution Neural Networks (CNN), AdaBoost and Bayesian classifiers is finally designed to precisely verify the extracted text regions. Our proposed technology is extensively evaluated on several public multi-orientation scene text datasets, e.g., MSRA-TD500 and USTB-SV1K, and is much better than state-of-the-art methods.