Convolutional Regression Network for Multi-Oriented Text Detection

Multi-oriented text detection in the wild is a challenging task due to the variations of scales, orientations, illumination, and languages. The traditional anchor mechanism on generic object detection can only generate horizontal proposals, which cannot be applied to detecting multi-oriented text regions. Considering this, in this paper, we propose a novel convolutional regression network (CRN) to localize multi-oriented text in natural images, which consists of two components: region proposal extractor and text locator. To be specific, we first present a hierarchical deconvolution module (HDM), a text-line and geometry segmentation module (TGM) to segment the multi-oriented proposals accurately, both of which are fully convolutional networks. Then, a classification and regression module (CRM) is adopted to process the proposals and obtain the final localization results. The whole framework can be trained in an end-to-end mechanism which is suitable for detecting multi-oriented texts. The extensive experiments are conducted on three mainstream scene-text datasets, and the experimental results evidence the proposed CRN achieves competitive performance.