Improving Scene Text Detection by Scale-Adaptive Segmentation and Weighted CRF Verification

This paper presents a hybrid method for detecting and localizing texts in natural scene images by stroke segmentation, verification and grouping. To improve system performance, novelties on two aspects are proposed: 1) a scale-adaptive segmentation method is designed for extracting stroke candidates, and 2) a CRF model with pair-wise weight by local line fitting is designed for stroke verification. Moreover, color-based text region estimation is used to guide segmentation and verification more accurately. Experimental results on ICDAR 2005 competition dataset show that the proposed approach can detect and localize scene texts with high accuracy, even under noisy and complex backgrounds.