Scene text detection with robust character candidate extraction method

The maximally stable extremal region (MSER) method has been widely used to extract character candidates, but because of its requirement for maximum stability, high text detection performance is difficult to obtain. To overcome this problem, we propose a robust character candidate extraction method that performs ER tree construction, sub-path partitioning, sub-path pruning, and character candidate selection sequentially. Then, we use the AdaBoost trained character classifier to verify the extracted character candidates. Then, we use heuristics to refine the classified character candidates and group the refined character candidates into text regions according to their geometric adjacency and color similarity. We also apply the proposed text detection method to two different color channels Cr and Cb and obtain the final detection result by combining the detection results on the three different channels. The proposed text detection method on ICDAR 2013 dataset achieved 8%, 1%, and 4% improvements in recall rate, precision rate and f-score, respectively, compared to the state-of-the-art methods.