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.
SSD-based method is highly stable and efficient in generating word proposals because SSD is one of the fastest object detector that is also as accurate as slower region-proposal based models like R-CNN. However, SSD possesses similar shortcomings in terms of anchor box design when it comes to scene-text detection. Thus, @cite_32 supplements SSD with "textbox layers" that can generate bounding-boxes with larger aspect ratios and simultaneously predict text presence and bounding boxes. Unfortunately, this method only works on horizontal texts, and not scene-texts. Thus in this paper, we attempt to solve the aforementioned limitations of previous detection models by utilizing a proposal-free method based on circular anchors and the SSD framework. Our method is computationally more efficient than both segmentation-based and Region-Proposal-based models because the removal of the region-proposal layer in our network. Our method also improves upon the existing SSD-based method by having the ability to detect both arbitrary-oriented texts and generic objects.
@cite_32
2550687635
This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression. TextBoxes outperforms competing methods in terms of text localization accuracy and is much faster, taking only 0.09s per image in a fast implementation. Furthermore, combined with a text recognizer, TextBoxes significantly outperforms state-of-the-art approaches on word spotting and end-to-end text recognition tasks.