BURSTS: A bottom-up approach for robust spotting of texts in scenes |
| |
| Affiliation: | 1. Academy for Engineering and Technology, Fudan University, Shanghai 200433, China;2. Shanghai Engineering Research Center of AI & Robotics, China;3. Engineering Research Center of AI & Robotics, Ministry of Education, China;4. School of Information Science and Technology, Fudan University, Shanghai 200433, China;5. Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA;1. Department of Cardiology, China-Japan Union Hospital of Jilin University, Changchun, China;2. Jilin Provincial Key Laboratory for Genetic Diagnosis of Cardiovascular Disease, Changchun, China;3. Jilin Provincial Molecular Biology Research Center for Precision Medicine of Major Cardiovascular Disease, Changchun, China;4. Department of Clinical Laboratory, China-Japan Union Hospital of Jilin University, Changchun, China;1. Department of Computer Science and Technology, Jilin University, Changchun 130012, China;2. Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education, Jilin University, Changchun 130012, China;3. Editorial Department of Journal (Engineering and Technology Edition), Jilin University, Jilin, Changchun 130012, China;1. Department of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;2. College of information, Liaoning University, Liaoning 110036, China |
| |
| Abstract: | In this paper, we present a bottom-up approach for robust spotting of texts in scenes. In the proposed technique, character candidates are first detected using our proposed character detector, which leverages on the strengths of an Extremal Region (ER) detector and an Aggregate Channel Feature (ACF) detector for high character detection recall. The real characters are then identified by using a novel convolutional neural network (CNN) filter for high character detection precision. A hierarchical clustering algorithm is designed which combines multiple visual and geometrical features to group characters into word proposal regions for word recognition. The proposed technique has been evaluated on several scene text spotting datasets and experiments show superior spotting performance. |
| |
| Keywords: | Text spotting CNN Extremal region Clustering 41A05 41A10 65D05 65D17 |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
