FASText: Efficient Unconstrained Scene Text Detector

@article{Busta2015FASTextEU,
  title={FASText: Efficient Unconstrained Scene Text Detector},
  author={Michal Busta and Luk{\'a}s Neumann and Jiri Matas},
  journal={2015 IEEE International Conference on Computer Vision (ICCV)},
  year={2015},
  pages={1206-1214}
}
We propose a novel easy-to-implement stroke detector based on an efficient pixel intensity comparison to surrounding pixels. [] Key Result When the proposed detector is plugged into a scene text localization and recognition pipeline, a state-of-the-art text localization accuracy is maintained whilst the processing time is significantly reduced.
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125 Citations
Highly Influential Citations
9
Background Citations
37
Methods Citations
41
Results Citations
2

125 Citations

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The authors present a simple and effective text detection method in natural scene image which is general for detecting scene text lines in different languages and achieves 82.94 and 75% F -measure.
Real-Time Text Localization in Natural Scene Images Using a Linear Spatial Filter
  • X. Gironés, Carme Julià
  • Computer Science
    2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
  • 2017
TLDR
The main contribution of the proposed method is its execution speed, being capable of processing 1080p HD video at nearly 30 frames per second on a standard laptop, and it delivers competitive results interms of precision and recall on the ICDAR 2013 Robust Reading dataset.
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Vesselness for text detection in historical document images
TLDR
A new method to detect text in images, particularly in historical document images is described, using the use of the vesselness filter as a new preprocessing step for text detection.
Fast Scene Text Detection with RT-LoG Operator and CNN
TLDR
The keypoint grouping method is proposed by first applying the real-time Laplacian of Gaussian operator (RT-LoG) to detect keypoints, which will be grouped to produce the character patterns.
EAST: An Efficient and Accurate Scene Text Detector
TLDR
This work proposes a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes, and significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency.
Morphology-based hierarchical representation with application to text segmentation in natural images
TLDR
A hierarchical image representation, based on the morphological Laplace operator, is proposed, which is used to give a robust text segmentation, and can be applied to document binarization, with the interesting feature of getting also reverse-video text.
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