We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| dafanghe/Tensorflow_SceneText_Oriented_Box_Predictor | extension | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| polygon_area | Ran | dafanghe/Tensorflow_SceneText_Oriented_Box_Predictor/create_text_dataset.py pointer only (licence: NONE) · get_code("88eef989a92fa8ff") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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. First, we use the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Second, for each axis-aligned text box proposed by RPN, we extract its pooled features with different pooled sizes and the concatenated features are used to simultaneously predict the text/non-text score, axis-aligned box and inclined minimum area box. At last, we use an inclined non-maximum suppression to get the detection results. Our approach achieves competitive results on text detection benchmarks: ICDAR 2015 and ICDAR 2013.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1706.09579")
get_code_for_paper("1706.09579")
have("1706.09579")
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