Fast and Accurate Text Detection in Natural Scene Images with User-Intention
Liuan Wang, Wei Fan, Yuan He, Jun Sun, Yutaka Katsuyama, Yoshinobu Hotta · 2014
Text detection in natural scene images plays an important role in content-based image retrieval, especially user-guided text detection for human-computer interaction. In this paper, we propose a fast and accurate text detection method with user-intention in terms of tap gesture. Firstly, a user-intention slice descriptor is designed based on the estimated text property, which contains all the user interested texts, and fast heuristic features and accurate texture feature of decomposed connected components (CCs) are fed into cascade of Gentle Adaboost classifiers to eliminate non-text candidates, finally candidate texts, sharing the same property consistent with the seed CCs, are accumulated to a user-intention text line according to local and global permutation constraint. Experimental results demonstrate the effectiveness and robustness of the proposed method in comparison with the state-of-art methods.