Papers › Synthetic Data for Text Localisation in Natural Images
Synthetic Data for Text Localisation in Natural Images
Ankush Gupta, Andrea Vedaldi, Andrew Zisserman
In this paper we introduce a new method for text detection in natural images. The method comprises two contributions: First, a fast and scalable engine to generate synthetic images of text in clutter. This engine overlays synthetic text to existing background images in a natural way, accounting for the local 3D scene geometry. Second, we use the synthetic images to train a Fully-Convolutional Regression Network (FCRN) which efficiently performs text detection and bounding-box regression at all locations and multiple scales in an image. We discuss the relation of FCRN to the recently-introduced YOLO detector, as well as other end-to-end object detection systems based on deep learning. The resulting detection network significantly out performs current methods for text detection in natural images, achieving an F-measure of 84.2% on the standard ICDAR 2013 benchmark. Furthermore, it can process 15 images per second on a GPU.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Text Detection | ICDAR 2013 | Gupta et al. | F-Measure | 83.0% | #12 of 16 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2013 | Gupta et al. | Precision | 92.0 | #12 of 16 | Archive leaderboard | report |
| Scene Text Detection | ICDAR 2013 | Gupta et al. | Recall | 75.5 | #12 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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