Curved Text Detection
7 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Curved Text Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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SCUT-CTW1500
Most implemented papers
EAST: An Efficient and Accurate Scene Text Detector
PaddlePaddle/PaddleOCR
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CVPR 2017
Previous approaches for scene text detection have already achieved promising performances across various benchmarks.
Shape Robust Text Detection with Progressive Scale Expansion Network
whai362/PSENet
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To address these problems, we propose a novel Progressive Scale Expansion Network (PSENet), designed as a segmentation-based detector with multiple predictions for each text instance.
Detecting Oriented Text in Natural Images by Linking Segments
It achieves an f-measure of 75. 0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin.
Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500.
Detecting Curve Text in the Wild: New Dataset and New Solution
Yuliang-Liu/Curve-Text-Detector
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To raise the concerns of reading curve text in the wild, in this paper, we construct a curve text dataset named CTW1500, which includes over 10k text annotations in 1, 500 images (1000 for training and 500 for testing).
Sliding Line Point Regression for Shape Robust Scene Text Detection
Shualite/UBNet
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Specifically, we first generate the smallest rectangular box including the text with region proposal network (RPN), then isometrically regress the points on the edge of text by using the vertically and horizontally sliding lines.
MTRNet: A Generic Scene Text Eraser
neouyghur/One-stage-Mask-based-Scene-Text-Eraser
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Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multi-lingual and curved text detection and inpainting.
