Improved Localization Accuracy by LocNet for Faster R-CNN Based Text Detection

Abstract

Although Faster R-CNN based approaches have achieved promising results for text detection, their localization accuracy is not satisfactory in certain cases. In this paper, we propose to use a LocNet to improve the localization accuracy of a Faster R-CNN based text detector. Given a proposal generated by region proposal network (RPN), instead of predicting directly the bounding box coordinates of the concerned text instance, the proposal is enlarged to create a search region so that conditional probabilities to each row and column of this search region can be assigned, which are then used to infer accurately the concerned bounding box. Experiments demonstrate that the proposed approach boosts the localization accuracy for Faster R-CNN based text detection significantly. Consequently, our new text detector has achieved superior performance on ICDAR-2011, ICDAR-2013 and MULTILIGUL text detection benchmark tasks.