REAL TIME MULTI-ORIENTED SCENE TEXT DETECTION

Abstract

Text detection in natural images is an important prerequisite for many content-based image analysis tasks. Although it is widely studied in recent years, due to unpredictable scene environment, reading texts is still quite challenging and continues to be an open research problem. It is recently being shown how the state-of-the-art object detection methods can be modi ed and then applied successfully for the purpose of scene text detection. A method, based upon YOLOv2 and RPN, intended to do end-to-end text recognition, achieves state of the art accuracy in the complete scene text recognition on two standard datasets ICDAR-2013 and ICDAR-2015, even while working at the real time and being faster than the other competing methods. This method is improved upon to give better text detection and localization results, in real time speeds.