Synthetic Data for Text Localisation in Natural Images

Ankush Gupta, Andrea Vedaldi, Andrew Zisserman; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2315-2324

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Gupta_2016_CVPR,
author = {Gupta, Ankush and Vedaldi, Andrea and Zisserman, Andrew},
title = {Synthetic Data for Text Localisation in Natural Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}