Skip to content

Latest commit

 

History

History
75 lines (53 loc) · 5.15 KB

README.md

File metadata and controls

75 lines (53 loc) · 5.15 KB
Raw

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.

Modifications to support other scripts:

Synthetic Scene-Text Image Samples Synthetic Scene-Text Samples

The code in the master branch is for Python2. Python3 is supported in the python3 branch.

The main dependencies are:

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

Generating samples

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

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://www.robots.ox.ac.uk/~vgg/data/scenetext/preproc/<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). md5 hash: 3eac26af5f731792c9d95838a23b5047 bg_img.tar.gz.
  • depth.h5 [15G]: depth maps. md5 hash: af97f6e6c9651af4efb7b1ff12a5dc1b depth.h5.
  • seg.h5 [6.9G]: segmentation maps. md5 hash: 1605f6e629b2524a3902a5ea729e86b2 seg.h5.

Note: due to large size, depth.h5 is also available for download as 3-part split-files of 5G each. These part files are named: depth.h5-00, depth.h5-01, depth.h5-02. Download using the path above, and put them together using cat depth.h5-0* > depth.h5.

use_preproc_bg.py provides sample code for reading this data.

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

Generating Samples with Text in non-Latin (English) Scripts

  • @JarveeLee has modified the pipeline for generating samples with Chinese text here.
  • @adavoudi has modified it for arabic/persian script, which flows from right-to-left here.
  • @MichalBusta has adapted it for a number of languages (e.g. Bangla, Arabic, Chinese, Japanese, Korean) here.
  • @gachiemchiep has adapted for Japanese here.

Further Information

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