A Single-Shot Oriented Scene Text Detector with Learnable Anchors

Current regression based text detectors mainly use fixed anchors, where scales and positions can not be changed during network training. As scene texts tend to have large variation in orientations, aspect ratios and sizes, fixed anchors are insufficient to cover all varieties. This paper proposes a novel text detector with learnable anchors, named LATD. LATD contains two prediction branches. One aims to refine scales and locations of anchors according to the characteristics of scene texts. The other one receives refined anchors as defaults and regresses their offsets to text regions. These two branches are optimized jointly without sacrifices much speed. Meanwhile, we explore the class-imbalance issue between texts and backgrounds, and replace softmax loss with focal loss. Extensive experiments on both oriented and horizontal benchmarks demonstrate the effectiveness of LATD with new state-of-the-art performance. By visualizing qualitative results, as expected, LATD provides more accurate locations and lower rate of missed detections.