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R2CNN is a novel framework that uses Faster R-CNN architecture to detect arbitrary-oriented texts in natural images. It extracts pooled features of different sizes and predicts text/non-text score, axis-aligned and inclined boxes for each text proposal.
Abstract: Scene text detection is challenging as the input may have different orientations, sizes, font styles, lighting conditions, perspective distortions and languages. This paper addresses the problem by designing a Rotational Region CNN (R 2 CNN). R 2 CNN includes a Text Region Proposal Network (Text-RPN) to estimate approximate text regions and a multitask refinement network to get the ...
R2CNN is a novel framework based on Faster R-CNN that can detect arbitrary-oriented texts in natural images. It uses RPN to propose axis-aligned text regions, ROIPooling to extract features, and inclined non-maximum suppression to refine the results.
A novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images using the Region Proposal Network to generate axis-aligned bounding boxes that enclose the texts with different orientations. In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images. The framework ...
R2CNN is a method for detecting arbitrary-oriented texts in natural scene images based on Faster R-CNN architecture. It uses pooled features with different sizes to predict text/non-text score, axis-aligned box and inclined minimum area box.
caffe re-implementation of R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection - beacandler/R2CNN
View a PDF of the paper titled R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy, by Xue Yang and 6 other authors. View PDF Abstract: Object detection plays a vital role in natural scene and aerial scene and is full of challenges. Although many advanced algorithms have succeeded in the natural ...
In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images. The framework is based on Faster R-CNN [1] architecture.
@inproceedings{yang2019scrdet, title={SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects}, author={Yang, Xue and Yang, Jirui and Yan, Junchi and Zhang, Yue and Zhan, Tengfei and Guo, Zhi and Xian, Sun and Fu, Kun}, booktitle={Proc. ICCV}, year={2019} } @article{yang2018position, title={Position Detection and Direction Prediction for Arbitrary-Oriented Ships via ...
A Tensorflow implementation of FPN or R2CNN detection framework based on FPN. You can refer to the papers R2CNN Rotational Region CNN for Orientation Robust Scene Text Detection or Feature Pyramid Networks for Object Detection Other rotation detection method reference R-DFPN, RRPN and R2CNN_HEAD If useful to you, please star to support my work ...