EasyOCR

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

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)

See Documentation

Run on command line

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

Implementation Roadmap

Phase 1 (Now - October, 2020)

Language packs: Hindi, Arabic, Cyrillic alphabet, etc. Aiming to cover > 80-90% of world's population. See current development list.
Better documentation and api
Language model for better decoding

Phase 2 (After October, 2020)

Handwritten support: Network architecture should not matter. The key is using GAN to generate realistic handwritten dataset.
Faster processing time: model pruning/quantization/export to other platforms
Data generation script and model training pipeline
Restructure code to support swappable detection and recognition algorithm. The api should be as easy as

reader = easyocr.Reader(['en'], detection='pixellink', recognition = 'ReXNet_LSTM_Attention')

The idea is to be able to plug-in any state-of-the-art model into EasyOCR. There are a lot of geniuses trying to make better detection/recognition model. We are not trying to be a genius here, just make genius's works quickly accessible to the public ... for free. (well I believe most geniuses want their work to create positive impact as fast/big as possible) The pipeline should be something like below diagram. Grey slots are placeholders for changeable light blue modules.

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

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.
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

API Documentation

`Reader` class

Base class for EasyOCR

Parameters

lang_list (list) - list of language code you want to recognize, for example ['ch_sim','en']. List of supported language code is here.

gpu (bool, string, default = True)

Attribute

lang_char - Show all available characters in current model

`readtext` method

Main method for Reader object. There are 4 groups of parameter: General, Contrast, Text Detection and Bounding Box Merging.

Parameters 1: General

image (string, numpy array, byte)

decoder (string, default = 'greedy') - options are 'greedy', 'beamsearch' and 'wordbeamsearch'.

beamWidth (int, default = 5)

batch_size (int, default = 1) - batch_size>1 will make EasyOCR faster but use more memory

workers (int, default = 0)

allowlist (string) - Force EasyOCR to recognize only subset of characters

blocklist (string) - Will be ignored if allowlist is given

detail (int, default = 1) - Set this to 0 for simple output

Parameters 2: Contrast

contrast_ths (float, default = 0.1)

adjust_contrast (float, default = 0.5)

filter_ths (float, default = 0.003)

Parameters 3: Text Detection (from CRAFT)

text_threshold (float, default = 0.7)

low_text (float, default = 0.4)

link_threshold (float, default = 0.4)

canvas_size (int, default = 2560)

mag_ratio (float, default = 1)

Parameters 4: Bounding Box Merging

slope_ths (float, default = 0.1)

ycenter_ths (float, default = 0.5)

height_ths (float, default = 0.5)

width_ths (float, default = 0.5)

add_margin (float, default = 0.1)

Return (list)

iceout/EasyOCR