|Rakpong Kittinaradorn 914b164626 Update recognition.py||20 hours ago|
|easyocr||20 hours ago|
|examples||2 weeks ago|
|scripts||3 weeks ago|
|.gitignore||4 months ago|
|Dockerfile||2 weeks ago|
|LICENSE||4 months ago|
|MANIFEST.in||3 months ago|
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|requirements.txt||1 week ago|
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|setup.py||20 hours ago|
Ready-to-use OCR with 40+ languages supported including Chinese, Japanese, Korean and Thai.
Readerinstance) (thanks @jpotter)
'paragraph' = True)
We are currently supporting the following 58 languages.
Afrikaans (af), Arabic (ar), Azerbaijani (az), Belarusian (be), Bulgarian (bg), Bosnian (bs), Simplified Chinese (ch_sim), Traditional Chinese (ch_tra), Czech (cs), Welsh (cy), Danish (da), German (de), English (en), Spanish (es), Estonian (et), Persian (Farsi) (fa) French (fr), Irish (ga), Hindi(hi), Croatian (hr), Hungarian (hu), Indonesian (id), Icelandic (is), Italian (it), Japanese (ja), Korean (ko), Kurdish (ku), Latin (la), Lithuanian (lt), Latvian (lv), Maori (mi), Mongolian (mn), Marathi (mr), Malay (ms), Maltese (mt), Nepali (ne), Dutch (nl), Norwegian (no), Occitan (oc), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Serbian (cyrillic)(rs_cyrillic), Serbian (latin)(rs_latin), Slovak (sk) (need revisit), Slovenian (sl), Albanian (sq), Swedish (sv), Swahili (sw), Thai (th), Tagalog (tl), Turkish (tr), Uyghur (ug), Ukranian(uk), Urdu (ur), 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).
pip for stable release,
pip install easyocr
For latest development release,
pip install git+git://github.com/jaidedai/easyocr.git
Note 1: for Windows, please install torch and torchvision first by following the official instruction here https://pytorch.org. On pytorch website, be sure to select the right CUDA version you have. If you intend to run on CPU mode only, select CUDA = None.
Note 2: We also provide Dockerfile here.
import easyocr reader = easyocr.Reader(['ch_sim','en']) # need to run only once to load model into memory result = 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)]
['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.
Note 3: The line
reader = easyocr.Reader(['ch_sim','en']) is for loading model into memory. It takes some time but it need to be run only once.
You can also set
detail = 0 for simpler output.
reader.readtext('chinese.jpg', detail = 0)
['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(['ch_sim','en'], gpu = False)
$ easyocr -l ch_sim en -f chinese.jpg --detail=1 --gpu=True
python reader = easyocr.Reader(['en'], detection='DB', recognition = 'CNN_Transformer')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.
This project is based on researches/codes from several papers/open-source repositories.
All deep learning part is based on Pytorch. :heart:
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 deep-text-recognition-benchmark. (Thanks @ku21fan from @clovaai) This repository is a gem that deserved more recognition.
And good read about CTC from distill.pub here.
Let’s advance humanity together by making AI available to everyone!
3 ways to contribute:
Coder: Please send PR for small bug/improvement. For bigger one, discuss with us by open an issue first. There is a list of possible bug/improvement issue tagged with ‘PR WELCOME’.
User: Tell us how EasyOCR benefit you/your organization to encourage further development. Also post failure cases in Issue Section to help improving future model.
Tech leader/Guru: If you found this library useful, please spread the word! (See Yann Lecun’s post about EasyOCR)
To request a new language support, I need you to send a PR with 2 following files
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.
Base class for EasyOCR
- 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)
- model_storage_directory (string, default = None)
- download_enabled (bool, default = True)
- lang_char - Show all available characters in current model
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) - Input image
- decoder (string, default = ‘greedy’) - options are ‘greedy’, ‘beamsearch’ and ‘wordbeamsearch’.
- beamWidth (int, default = 5) - How many beam to keep when decoder = ‘beamsearch’ or ‘wordbeamsearch’
- batch_size (int, default = 1) - batch_size>1 will make EasyOCR faster but use more memory
- workers (int, default = 0) - Number thread used in of dataloader
- allowlist (string) - Force EasyOCR to recognize only subset of characters. Useful for specific problem (E.g. license plate, etc.)
- blocklist (string) - Block subset of character. This argument will be ignored if allowlist is given.
- detail (int, default = 1) - Set this to 0 for simple output
- paragraph (bool, default = False) - Combine result into paragraph
Parameters 2: Contrast
- contrast_ths (float, default = 0.1) - Text box with contrast lower than this value will be passed into model 2 times. First is with original image and second with contrast adjusted to ‘adjust_contrast’ value. The one with more confident level will be returned as a result.
- adjust_contrast (float, default = 0.5) - target contrast level for low contrast text box
Parameters 3: Text Detection (from CRAFT)
- text_threshold (float, default = 0.7) - Text confidence threshold
- low_text (float, default = 0.4) - Text low-bound score
- link_threshold (float, default = 0.4) - Link confidence threshold
- canvas_size (int, default = 2560) - Maximum image size. Image bigger than this value will be resized down.
- mag_ratio (float, default = 1) - Image magnification ratio
Parameters 4: Bounding Box Merging
This set of parameter controls when adjacent bounding boxes merge with each other. Every parameters except ‘slope_ths’ is in the unit of box height.
- slope_ths (float, default = 0.1) - Maximum slope (delta y/delta x) to considered merging. Low value means tiled boxes will not be merged.
- ycenter_ths (float, default = 0.5) - Maximum shift in y direction. Boxes with different level should not be merged.
- height_ths (float, default = 0.5) - Maximum different in box height. Boxes with very different text size should not be merged.
- width_ths (float, default = 0.5) - Maximum horizontal distance to merge boxes.
- add_margin (float, default = 0.1) - Extend bounding boxes in all direction by certain value. This is important for language with complex script (E.g. Thai).