Jaided Read

End-to-End Multilingual Optical Character Recognition (OCR) Solution

Supported Languages

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.

Installation

Install using pip for stable release,

pip install jaidedread

For latest development release,

pip install git+git://github.com/jaidedai/jaidedread.git

Usage

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)]

See full documentation at https://jaided.ai/read/doc

To be implemented

1. Language packs: Chinese, Japanese, Korean group + Russian-based languages + Arabic + etc.
2. Language model for better decoding
3. Better documentation and api

Acknowledgement and References

This project is based on researches/codes from several papers/open-source repositories.

Detection part is using CRAFT algorithm from this official repository and their paper.

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.

Beam search code is based on this repository and his blog.

And good read about CTC from distill.pub here.

Citations

If you use jaidedread in your project/research, please consider citing the library as follows ... (link to be created)