Awesome

SynthText with German Language Support

Modified from here to support german characters and text. The project was carried out during an internship at the Fraunhofer-Institute for Production Technology and Automation (IPA) in Stuttgart.

Scene-Text Image Samples generated with the code

Environment

OS: Windows10

python==3.8.5

opencv==4.5.1

pygame==1.9.6

Adjustments to support german text

• added german text source with 3 million sentences taken from 2015 newspaper texts

  • integrated it in text_utils.py within the class RenderFont()
  • set the encoding to utf-8 in class TextSource()
  • created corresponding character frequency model with update_freq.py

• added fonts with umlauts (most of them were google fonts) and updated the fontlist.txt

  • created corresponding font model with invert_font_size.py
  • integrated font model in text_utils.py within the class FontState

Usage Steps

1. Run the script add_more_data.py to download the pre-processed background images with their depth and segmentation masks and to merge them into one h5 file.

   If downloading with add_more_data.py doesn't work you can use wget in git bash terminal to download them manually (more information to use wget on windows see here).

2. Run gen_more.py to generate new synthetic scene text images withe the pre-processed data.

   Or run gen_more.py --viz to get a visualization after each generated sample.

3. Visualize your results with visualize_results.py.

• If you have the same issue as described in issue #105 you can use the test_fonts.py to see which fonts are the reason for this problem.

Data Structure

data
├── bg_img                                   : pre-processed images
├── fonts
│   ├── ubuntu.ttf
│   ├── ...                                  : added fonts
│   └── fontlist.txt                         : updated fontlist
├── german_textSource
│   ├── 3M_sentences_LeipzigCorpora.txt      : added text source
│   └── words_LeipzigCorpora.csv
├── models
│   ├── char_freq.cp                         : updated character model
│   ├── colors_new.cp
│   └── font_px2pt.cp                        : updated font model
├── newsgroup
│   └── newsgroup.txt
├── depth.h5
├── dset_8000.h5                             : pre-processed data [img, depth, seg]
├── dset.h5
└── seg.h5

Parameter Settings

• number of images: in gen_more.py line 24

• rendering words, lines or paragraphs: in text_utils.py line 86

  • max words for lines/paragraphs: in text_utils.py line 532

• font state (curved, underlined, etc.): in text_utils.py line 400

• text regions: in synthgen.py line 32, line 380 and line 681

The rest of the README is from the original repository

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 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:

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.
• @gungui98 has adapted for Vietnamese here.
• @youngkyung has adapted for Korean here.

Further Information

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