Generate text images for training deep learning ocr model.

The code was only tested on Ubuntu 16.04.

Install dependencies:

pip3 install -r requirements.txt

Run python3 main.py, images and labels.txt will generate at output/default/

Some optional arguments:

• num_img: how many images to generate
• output_dir: where to save the images
• corpus_dir: put txt file in corpus_dir
• corpus_mode: different corpus type have different load/get_sample method, see corresponding function for detail
• chars_file: chars not contained in chars_file will be filtered
• bg_dir: 50% image background are loaded from background image dir
• line: add underline, crop from table line, middle highlight
• noise: add gauss noise, uniform, salt/pepper noise, poisson noise

There are a lot of configs used in renderer.py, you should change it to meet your own requirements.

If some chars in corpus is not supported by your font, your will get bad result:

Run main.py with --strict, renderer will retry get sample from corpus until all chars are supported by a font.

Check how many chars your font not support for a charset:

python3 tools/check_font.py

checking font ./data/fonts/eng/Hack-Regular.ttf
chars not supported(4971):
['第', '朱', '广', '沪', '联', '自', '治', '县', '驼', '身', '进', '行', '纳', '税', '防', '火', '墙', '掏', '心', '内', '容', '万', '警','钟', '上', '了', '解'...]
0 fonts support all chars(5071) in ./data/chars/chn.txt:
[]

Run python3 main.py --debug will save images with extract information. You can see how perspectiveTransform works and all bounding/rotated boxes.

If you want to use GPU to speed up image generating, first compile opencv with CUDA. Compiling OpenCV with CUDA support

Then build Cython part, and add --gpu options when run main.py

cd libs/gpu
python3 setup.py build_ext --inplace

See https://github.com/Sanster/text_renderer/projects/1