Easy OCR

Ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai.

Examples

See this Colab Demo. You can run it in the browser.

Supported Languages

We are currently supporting the following 45 languages.

Afrikaans (af), Azerbaijani (az), Bosnian (bs), Simplified Chinese (ch_sim), Traditional Chinese (ch_tra), 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), Japanese (ja), Korean (ko), Kurdish (ku), Latin (la), Lithuanian (lt), Latvian (lv), Maori (mi), Malay (ms), Maltese (mt), Dutch (nl), Norwegian (no), Occitan (oc), Polish (pl), Portuguese (pt), Romanian (ro), Serbian (latin)(rs_latin), Slovak (sk) (need revisit), Slovenian (sl), Albanian (sq), Swedish (sv),Swahili (sw), Thai (th), Tagalog (tl), Turkish (tr), Uzbek (uz), Vietnamese (vi) (need revisit)

List of characters is in folder easyocr/character. If you are native speaker of any language and think we should add or remove any character, please create an issue and/or pull request (like this one).

Installation

Install using pip for stable release,

pip install easyocr

For latest development release,

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

Note: for Windows, please install torch and torchvision first by following official instruction here https://pytorch.org

Usage

import easyocr
reader = easyocr.Reader(['ch_sim','en'])
reader.readtext('chinese.jpg')

Output will be in list format, each item represents bounding box, text and confident level, respectively.

[([[189, 75], [469, 75], [469, 165], [189, 165]], '愚园路', 0.3754989504814148),
 ([[86, 80], [134, 80], [134, 128], [86, 128]], '西', 0.40452659130096436),
 ([[517, 81], [565, 81], [565, 123], [517, 123]], '东', 0.9989598989486694),
 ([[78, 126], [136, 126], [136, 156], [78, 156]], '315', 0.8125889301300049),
 ([[514, 126], [574, 126], [574, 156], [514, 156]], '309', 0.4971577227115631),
 ([[226, 170], [414, 170], [414, 220], [226, 220]], 'Yuyuan Rd.', 0.8261902332305908),
 ([[79, 173], [125, 173], [125, 213], [79, 213]], 'W', 0.9848111271858215),
 ([[529, 173], [569, 173], [569, 213], [529, 213]], 'E', 0.8405593633651733)]

Note 1: ['ch_sim','en'] is the list of languages you want to read. You can pass several languages at once but not all languages can be used together. English is compatible with every languages. Languages that share common characters are usually compatible with each other.

Note 2: Instead of filepath chinese.jpg, you can also pass OpenCV image object (numpy array) or image file as bytes. URL to raw image is also acceptable.

You can also set detail = 0 for simpler output.

reader.readtext('chinese.jpg', detail = 0)

Result:

['愚园路', '西', '东', '315', '309', 'Yuyuan Rd.', 'W', 'E']

Model weight for chosen language will be automatically downloaded or you can download it manually from the following links and put it in '~/.EasyOCR/model' folder

• text detection model
• latin model
• chinese (traditional) model
• chinese (simplified) model
• japanese model
• korean model
• thai model

In case you do not have GPU or your GPU has low memory, you can run it in CPU mode by adding gpu = False

reader = easyocr.Reader(['th','en'], gpu = False)

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 workers and batch_size. Current version converts image into grey scale for recognition model, so contrast can be an issue. You can try playing with contrast_ths, adjust_contrast and filter_ths. allowlist and blocklist accept input in string (like this blocklist = '!&$%').

Run on command line

$ easyocr -l ch_sim en -f chinese.jpg --detail=1 --gpu=True

To be implemented

1. Language packs: Hindi, Arabic, Cyrillic alphabet, 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.

Want To Contribute?

Let's advance humanity together by making AI available to everyone!

Please create issue to report bug or suggest new feature. Pull requests are welcome. Or if you found this library useful, just tell your friend about it.

Guideline for new language request

To request a new language support, I need you to send a PR with 2 following files

1. In folder easyocr/character, we need 'yourlanguagecode_char.txt' that contains list of all characters. Please see format example from other files in that folder.
2. In folder easyocr/dict, we need 'yourlanguagecode.txt' that contains list of words in your language. On average we have ~30000 words per language with more than 50000 words for popular one. More is better in this file.

If your language has unique elements (such as 1. Arabic: characters change form when attach to each other + write from right to left 2. Thai: Some characters need to be above the line and some below), please educate me with your best ability and/or give useful links. It is important to take care of the detail to achieve a system that really works.

Lastly, please understand that my priority will have to go to popular language or set of languages that share most of characters together (also tell me if your language share a lot of characters with other). It takes me at least a week to work for new model. You may have to wait a while for new model to be released.

See List of languages in development