SynthText

Code for generating synthetic text images as described in "Synthetic Data for Text Localisation in Natural Images", Ankush Gupta, Andrea Vedaldi, Andrew Zisserman, CVPR 2016 with some modifications.

Pre-generated Dataset

A dataset with approximately 800000 synthetic scene-text images generated with this code can be found here.

Adding New Images

Segmentation and depth-maps are required to use new images as background. Sample scripts for obtaining these are available here.

• predict_depth.m MATLAB script to regress a depth mask for a given RGB image; uses the network of Liu etal. However, more recent works (e.g., this) might give better results.
• run_ucm.m and floodFill.py for getting segmentation masks using gPb-UCM.

For an explanation of the fields in dset.h5 (e.g.: seg,area,label), please check this comment.

Pre-processed Background Images

The 8,000 background images used in the paper, along with their segmentation and depth masks, have been uploaded here: http://zeus.robots.ox.ac.uk/textspot/static/db/<filename>, where, <filename> can be:

• imnames.cp [180K]: names of filtered files, i.e., those files which do not contain text
• bg_img.tar.gz [8.9G]: compressed image files (more than 8000, so only use the filtered ones in imnames.cp)
• depth.h5 [15G]: depth maps
• seg.h5 [6.9G]: segmentation maps

Note: I do not own the copyright to these images.

Download corpus and font/color models

wget http://www.robots.ox.ac.uk/~ankush/data.tar.gz

tar -xvf data.tar.gz

To generate images follow instructions in generate_synth_data.ipynb