• Corpus ID: 85502420

Curve Text Detection with Local Segmentation Network and Curve Connection

  title={Curve Text Detection with Local Segmentation Network and Curve Connection},
  author={Zhao Zhou and Shufan Wu and Shuchen Kong and Yingbin Zheng and Hao Ye and Luhui Chen and Jian Pu},
Curve text or arbitrary shape text is very common in real-world scenarios. In this paper, we propose a novel framework with the local segmentation network (LSN) followed by the curve connection to detect text in horizontal, oriented and curved forms. The LSN is composed of two elements, i.e., proposal generation to get the horizontal rectangle proposals with high overlap with text and text segmentation to find the arbitrary shape text region within proposals. The curve connection is then… 
1 Citations

Figures and Tables from this paper

Scene Text Detection in Natural Images: A Review
The history and progress of scene text detection is introduced and the traditional methods and deep learning-based methods in detail are classified in detail, pointing out the corresponding key issues and techniques.


Arbitrary-Oriented Scene Text Detection via Rotation Proposals
The Rotation Region Proposal Networks are designed to generate inclined proposals with text orientation angle information that are adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation.
Shape Robust Text Detection With Progressive Scale Expansion Network
A novel Progressive Scale Expansion Network (PSENet) is proposed, which can precisely detect text instances with arbitrary shapes and is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances.
Detecting Curve Text in the Wild: New Dataset and New Solution
A polygon based curve text detector (CTD) which can directly detect curve text without empirical combination by seamlessly integrating the recurrent transverse and longitudinal offset connection (TLOC), which allows the CTD to explore context information instead of predicting points independently, resulting in more smooth and accurate detection.
Sliding Line Point Regression for Shape Robust Scene Text Detection
  • Yixing Zhu, Jun Du
  • Computer Science
    2018 24th International Conference on Pattern Recognition (ICPR)
  • 2018
This study proposes a novel method named sliding line point regression (SLPR) in order to detect arbitrary-shape text in natural scene and achieved competitive results on traditional ICDAR2015 Incidental Scene Text benchmark and curve text detection dataset CTW1500.
Detecting Text in Natural Image with Connectionist Text Proposal Network
A novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image and develops a vertical anchor mechanism that jointly predicts location and text/non-text score of each fixed-width proposal, considerably improving localization accuracy.
Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
To evaluate its robustness against curved text, DeconvNet is fine-tuned and benchmarked on Total-Text to facilitate a new research direction for the scene text community.
Detecting Oriented Text in Natural Images by Linking Segments
SegLink, an oriented text detection method to decompose text into two locally detectable elements, namely segments and links, achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin.
Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection
  • Yuliang Liu, Lianwen Jin
  • Computer Science
    2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
A new Convolutional Neural Networks (CNNs) based method, named Deep Matching Prior Network (DMPNet), to detect text with tighter quadrangle, which has better overall performance than L2 loss and smooth L1 loss in terms of robustness and stability.
TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes
A more flexible representation for scene text is proposed, termed as TextSnake, which is able to effectively represent text instances in horizontal, oriented and curved forms and outperforms the baseline on Total-Text by more than 40% in F-measure.
Text Flow: A Unified Text Detection System in Natural Scene Images
The proposed unified scene text detection system, namely Text Flow, is proposed by utilizing the minimum cost (min-cost) flow network model and it outperforms the state-of-the-art methods on all three datasets with much higher recall and F-score.