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.

** Synthetic Scene-Text Image Samples**

The library is written in Python. The main dependencies are:

pygame, opencv (cv2), PIL (Image), numpy, matplotlib, h5py, scipy

python gen.py --viz

This will download a data file (~56M) to the data directory. This data file includes:

• dset.h5: This is a sample h5 file which contains a set of 5 images along with their depth and segmentation information. Note, this is just given as an example; you are encouraged to add more images (along with their depth and segmentation information) to this database for your own use.
• data/fonts: three sample fonts (add more fonts to this folder and then update fonts/fontlist.txt with their paths).
• data/newsgroup: Text-source (from the News Group dataset). This can be subsituted with any text file. Look inside text_utils.py to see how the text inside this file is used by the renderer.
• data/models/colors_new.cp: Color-model (foreground/background text color model), learnt from the IIIT-5K word dataset.
• data/models: Other cPickle files (char_freq.cp: frequency of each character in the text dataset; font_px2pt.cp: conversion from pt to px for various fonts: If you add a new font, make sure that the corresponding model is present in this file, if not you can add it by adapting invert_font_size.py).

This script will generate random scene-text image samples and store them in an h5 file in results/SynthText.h5. If the --viz option is specified, the generated output will be visualized as the script is being run; omit the --viz option to turn-off the visualizations. If you want to visualize the results stored in results/SynthText.h5 later, run:

python visualize_results.py

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

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.

Please refer to the paper for more information, or contact me (email address in the paper).