Scene Text Detection with Adaptive Line Clustering

Xinxu Qiao, He Zhu, Weiping Li

Abstract: We propose a scene text detection system which can maintain a high recall while achieving a fair precision. In our method, no character candidate is eliminated based on character-level features. A weighted directed graph is constructed and the minimum average cost path algorithm is adopted to extract line candidates. After assigning three line-level probability values to each line, the final decisions are made according to the line candidate clustering of the current image. The proposed system has been evaluated on the ICDAR 2013 dataset. Compared with other published methods, it has achieved better performances.