This fork is intended to be used for generating synthetic text images for detection and recognition tasks, mainly to use with docTR.
List of changes compared to the original repository:
- Updated the original code for compatibility with Python 3.10 and fixed
numpyandpillowversions to prevent dependency conflicts. - Implemented a new Corpus class to sample words deterministically from the prepared corpus file.
- Developed a new Fit class to enable the generation of multi-line text images with associated polygon coordinates.
- Utilised NumPy broadcasting to evaluate text visibility, significantly accelerating the generation process.
- Removed specific blend modes that resulted in illegible or invisible rendered text.
- New templates and configuration files to produce synthetic images tailored for docTR model training.
- Integrated tools for cleaning Arabic corpus files and converting output data into the requisite docTR format.
- Curated and uploaded additional texture images to enhance data augmentation for the text detection task.
Compatible with Python 3.10. Follow the next steps to set up the environment and install the library (don't install the library from PyPI or using the original repository instructions, install it from this source code):
git clone git@github.com:tarekio/synthtiger.git
cd synthtiger
python -m venv .venv # or python3.10 -m venv .venv
source .venv/bin/activate
pip install . # or python3.10 -m pip install .
You need to replace corpus file resources/corpus/corpus.txt with your own corpus file. The format of the corpus file is one word per line. You can get free corpora from Leipzig Corpora Collection. tools/clean_arabic_sentences.py and tools/process_txt.py can be used for cleaning and processing Arabic corpus files. Feel free to edit to suit your needs/language.
You should also add your own font files in resources/font directory, if needed. You can get free fonts from Google Fonts. Check the original readme below for more details on adding custom fonts.
To generate synthetic text images for recognition task, run the following command.
synthtiger -o reco -c 10 -w 2 -v templates/reco.py Reco templates/reco.yaml # c is the number of images to generate, w is the number of workers.
Check the template and config files for more details. The existing template will generate single word images, going through the corpus file line by line from the beginning. To change this behavior, you can edit the template file templates/reco.py and replace the corpus class with the original BaseCorpus.
Another point to note is that the template doesn't apply any augmentation, it just generates clean text images. You can add augmentations in the template file if you want, or you can apply augmentations later in the training pipeline.
Here are examples of generated images for recognition task:
The generated data will be saved in reco directory using the default structure outlined below.
Once you're done, you can convert the generated data to the format docTR expects:
python tools/gt_to_json.py reco/gt.txt /path/to/dataset/
To generate synthetic text images for detection task, run the following command.
synthtiger -o detect -c 10 -w 2 -v templates/detect.py Detect templates/detect.yaml
Check the template and config files for more details. The existing template will generate multi-line text images, going through the corpus file randomly. The template will save polygon coordinates of each word, rather than each character as the original repository does.
The template applies texture augmentation and post-processing, but doesn't apply any transformation augmentation. You can add uncomment transforms in the template and config files if you want, or you can apply augmentations later in the training pipeline.
The images will contain background textures from resources/images_pics directory, with the aim of generating realistic images of documents taken by mobile phones. You can change the texture images or add more texture images. You can also add scanned document images from resources/images_scan directory. Check FUNSD dataset for examples of scanned document images.
Here are examples of generated images for detection task with polygon annotations in green:
The generated data will be saved in detect directory using the default structure outlined below.
coords.txt will contain file path, image size and polygon coordinates of each word in the generated images.
Once you're done, you can convert the generated data to the format docTR expects:
python tools/coord_to_json.py detect /path/to/dataset/
Below is the original readme from the repository, which contains more details on the usage of the library and how to customize it for your needs.
Synthetic Text Image Generator for OCR Model | Paper | Documentation | Datasets
The documentation is available at https://clovaai.github.io/synthtiger/.
You can check API reference in this documentation.
SynthTIGER requires python>=3.6 and libraqm.
To install SynthTIGER from PyPI:
$ pip install synthtigerIf you see a dependency error when you install or run SynthTIGER, install dependencies.
# Set environment variable (for macOS)
$ export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YESusage: synthtiger [-h] [-o DIR] [-c NUM] [-w NUM] [-s NUM] [-v] SCRIPT NAME [CONFIG]
positional arguments:
SCRIPT Script file path.
NAME Template class name.
CONFIG Config file path.
optional arguments:
-h, --help show this help message and exit
-o DIR, --output DIR Directory path to save data.
-c NUM, --count NUM Number of output data. [default: 100]
-w NUM, --worker NUM Number of workers. If 0, It generates data in the main process. [default: 0]
-s NUM, --seed NUM Random seed. [default: None]
-v, --verbose Print error messages while generating data.
# horizontal
synthtiger -o results -w 4 -v examples/synthtiger/template.py SynthTiger examples/synthtiger/config_horizontal.yaml
# vertical
synthtiger -o results -w 4 -v examples/synthtiger/template.py SynthTiger examples/synthtiger/config_vertical.yaml
images: a directory containing images.gt.txt: a file containing text labels.coords.txt: a file containing bounding boxes of characters with text effect.glyph_coords.txt: a file containing bounding boxes of characters without text effect.masks: a directory containing mask images with text effect.glyph_masks: a directory containing mask images without text effect.
synthtiger -o results -w 4 -v examples/multiline/template.py Multiline examples/multiline/config.yaml
images: a directory containing images.gt.txt: a file containing text labels.
-
Prepare corpus
txtformat, line by line (example). -
Prepare fonts
See font customization for more details.
-
Edit corpus path and font path in config file (example)
-
Run synthtiger
-
Prepare fonts
ttf/otfformat (example). -
Extract renderable charsets
python tools/extract_font_charset.py -w 4 fonts/
This script extracts renderable charsets for all font files (example).
Text files are generated in the input path with the same names as the fonts.
-
Edit font path in config file (example)
-
Run synthtiger
-
Prepare images
jpg/jpeg/png/bmpformat. -
Create colormaps
python tools/create_colormap.py --max_k 3 -w 4 images/ colormap.txt
This script creates colormaps for all image files (example).
-
Edit colormap path in config file (example)
-
Run synthtiger
You can implement custom templates by inheriting the base template.
from synthtiger import templates
class MyTemplate(templates.Template):
def __init__(self, config=None):
# initialize template.
def generate(self):
# generate data.
def init_save(self, root):
# initialize something before save.
def save(self, root, data, idx):
# save data to specific path.
def end_save(self, root):
# finalize something after save.SynthTIGER is available for download at google drive.
Dataset was split into several smaller files. Please download all files and run following command.
# for Linux, macOS
cat synthtiger_v1.0.zip.* > synthtiger_v1.0.zip
# for Windows
copy /b synthtiger_v1.0.zip.* synthtiger_v1.0.zipsynthtiger_v1.0.zip (36G) (md5: 5b5365f4fe15de24e403a9256079be70)
- Original paper version.
- Used MJ and ST label.
synthtiger_v1.1.zip (38G) (md5: b2757a7e2b5040b14ed64c473533b592)
- Used MJ and ST lexicon instead of MJ and ST label.
- Fixed a bug that applies transformation twice on curved text.
- Fixed a bug that incorrectly converts grayscale to RGB.
| Version | IIIT5k | SVT | IC03 | IC13 | IC15 | SVTP | CUTE80 | Total |
|---|---|---|---|---|---|---|---|---|
| 1.0 | 93.2 | 87.3 | 90.5 | 92.9 | 72.1 | 77.7 | 80.6 | 85.9 |
| 1.1 | 93.4 | 87.6 | 91.4 | 93.2 | 73.9 | 77.8 | 80.6 | 86.6 |
The structure of the dataset is as follows. The dataset contains 10M images.
gt.txt
images/
0/
0.jpg
1.jpg
...
9998.jpg
9999.jpg
1/
...
998/
999/
The format of gt.txt is as follows. Image path and label are separated by tab. (<image_path>\t<label>)
images/0/0.jpg 10
images/0/1.jpg date:
...
images/999/9999998.jpg STUFFIER
images/999/9999999.jpg Re:
@inproceedings{yim2021synthtiger,
title={SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models},
author={Yim, Moonbin and Kim, Yoonsik and Cho, Han-Cheol and Park, Sungrae},
booktitle={International Conference on Document Analysis and Recognition},
pages={109--124},
year={2021},
organization={Springer}
}SynthTIGER
Copyright (c) 2021-present NAVER Corp.
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of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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The following directories and their subdirectories are licensed the same as their origins. Please refer to NOTICE
docs/
resources/font/