Robust Text Detection with Vertically-Regressed Proposal Network

Methods for general object detection, such as R-CNN [4] and Fast R-CNN [3], have been successfully applied to text detection, as in [7]. However, there exists difficulty when directly using RPN [10], which is a leading object detection method, for text detection. This is due to the difference between text and general objects. On one hand, text regions have variable lengths, and thus networks must be designed to have large receptive field sizes. On the other hand, positive text regions cannot be measured in the same way as that for general objects at training. In this paper, we introduce a novel vertically-regressed proposal network (VRPN), which allows text regions to be matched by multiple neighboring small anchors. Meanwhile, training regions are selected according to how much they overlap with ground-truth boxes vertically and the location of positive regions is regressed only in the vertical direction. Experiments on dataset provided by ICDAR 2015 Challenge 1 demonstrate the effectiveness of our methods.