ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene
This addresses the problem of detecting text in varied orientations and conditions for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles arbitrary-oriented text detection in unconstrained scenes by proposing a novel method using circle anchors and a pyramid pooling module, achieving superior performance on datasets like ICDAR 2015 and MSRA-TD500.
Arbitrary-oriented text detection in the wild is a very challenging task, due to the aspect ratio, scale, orientation, and illumination variations. In this paper, we propose a novel method, namely Arbitrary-oriented Text (or ArbText for short) detector, for efficient text detection in unconstrained natural scene images. Specifically, we first adopt the circle anchors rather than the rectangular ones to represent bounding boxes, which is more robust to orientation variations. Subsequently, we incorporate a pyramid pooling module into the Single Shot MultiBox Detector framework, in order to simultaneously explore the local and global visual information, which can, therefore, generate more confidential detection results. Experiments on established scene-text datasets, such as the ICDAR 2015 and MSRA-TD500 datasets, have demonstrated the supe rior performance of the proposed method, compared to the state-of-the-art approaches.