End-to-End Multilingual Optical Character Recognition (OCR) Solution.
We are currently supporting following 39 languages.
Afrikaans (af), Azerbaijani (az), Bosnian (bs), Czech (cs), Welsh (cy),
Danish (da), German (de), English (en), Spanish (es), Estonian (et),
French (fr), Irish (ga), Croatian (hr), Hungarian (hu), Indonesian (id),
Icelandic (is), Italian (it), Kurdish (ku), Latin (la), Lithuanian (lt),
Latvian (lv), Maori (mi), Malay (ms), Maltese (mt), Dutch (nl), Norwegian (no),
Polish (pl), Portuguese (pt),Romanian (ro), Slovak (sk), Slovenian (sl),
Albanian (sq), Swedish (sv),Swahili (sw), Thai (th), Tagalog (tl),
Turkish (tr), Uzbek (uz), Vietnamese (vi)
List of characters is in folder jaidedread/character. If you are native speaker
of any language and think we should add or remove any character,
please create an issue.
pip for stable release,
pip install jaidedread
For latest development release,
pip install git+git://github.com/jaidedai/jaidedread.git
import jaidedread reader = jaidedread.Reader(['th','en']) reader.readtext('test.jpg')
Model weight for chosen language will be automatically downloaded or you can
download it manually from https://jaided.ai/read_download and put it
in 'model' folder.
Output will be in list format, each item represents bounding box, text and confident level, respectively.
[([[1344, 439], [2168, 439], [2168, 580], [1344, 580]], 'ใจเด็ด', 0.4542357623577118), ([[1333, 562], [2169, 562], [2169, 709], [1333, 709]], 'Project', 0.9557611346244812)]
There are optional arguments for readtext function,
decoder can be 'greedy'(default), 'beamsearch', or 'wordbeamsearch'. For 'beamsearch' and 'wordbeamsearch', you can also set
beamWidth (default=5). Bigger number will be slower but can be more accurate. For multiprocessing, you can set set
batch_size. Current version converts image into grey scale for recognition model. So contrast can be an issue. You can try playing with
See full documentation at https://jaided.ai/read/doc
To be implemented
- Language packs: Chinese, Japanese, Korean group + Russian-based languages +
Arabic + etc.
- Language model for better decoding
- Better documentation and api
Acknowledgement and References
This project is based on researches/codes from several papers/open-source repositories.
Recognition model is CRNN (paper). It is composed of 3 main components, feature extraction (we are currently using Resnet), sequence labeling (LSTM) and decoding (CTC). Training pipeline for recognition part is a modified version from this repository.
And good read about CTC from distill.pub here.