# Synthetic Data for Text Localisation in Natural Images

arXiv: https://arxiv.org/abs/1604.06646 · HTML: https://mlsanity.com/paper/1604.06646/

## Key facts

- Cited by 396 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2016.

## Built on by (most cited first, 30 of 396)

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## Abstract (arXiv)

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.