Recent Update
- 2021.4.9 supports the detection and recognition of 80 languages
- 2021.4.9 supports lightweight high-precision English model detection and recognition
PaddleOCR aims to create a rich, leading, and practical OCR tool library, which not only provides Chinese and English models in general scenarios, but also provides models specifically trained in English scenarios. And multilingual models covering 80 languages.
Among them, the English model supports the detection and recognition of uppercase and lowercase letters and common punctuation, and the recognition of space characters is optimized:
The multilingual models cover Latin, Arabic, Traditional Chinese, Korean, Japanese, etc.:
This document will briefly introduce how to use the multilingual model.
PaddleOCR provides two installation methods
# cpu
pip install paddlepaddle
# gpu
pip install paddlepaddle-gpu
pip install
pip install "paddleocr>=2.0.6" # 2.0.6 version is recommended
Build and install locally
python3 setup.py bdist_wheel
pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x is the version number of paddleocr
- View help information
paddleocr -h
- Whole image prediction (detection + recognition)
PaddleOCR currently supports 80 languages, which can be switched by modifying the --lang parameter. The specific supported language can be viewed in the table.
paddleocr --image_dir doc/imgs_en/254.jpg --lang=en
The result is a list, each item contains a text box, text and recognition confidence
[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
[('8022256183', 0.99810505), [[71.0, 175.0], [363.0, 170.0], [364.0, 202.0], [72.0, 207.0]]]
[('REG 07-24-201706:59 PM', 0.93537045), [[73.0, 299.0], [653.0, 281.0], [654.0, 318.0], [74.0, 336.0]]]
[('045555', 0.99346405), [[509.0, 331.0], [651.0, 325.0], [652.0, 356.0], [511.0, 362.0]]]
[('CT1', 0.9988654), [[535.0, 367.0], [654.0, 367.0], [654.0, 406.0], [535.0, 406.0]]]
......
- Recognition
paddleocr --image_dir doc/imgs_words_en/word_308.png --det false --lang=en
The result is a tuple, which returns the recognition result and recognition confidence
(0.99879867, 'LITTLE')
- Detection
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
The result is a list, each item contains only text boxes
[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
......
PaddleOCR also supports running in python scripts for easy embedding in your own code:
- Whole image prediction (detection + recognition)
from paddleocr import PaddleOCR, draw_ocr
# Also switch the language by modifying the lang parameter
ocr = PaddleOCR(lang="korean") # The model file will be downloaded automatically when executed for the first time
img_path ='doc/imgs/korean_1.jpg'
result = ocr.ocr(img_path)
# Recognition and detection can be performed separately through parameter control
# result = ocr.ocr(img_path, det=False) Only perform recognition
# result = ocr.ocr(img_path, rec=False) Only perform detection
# Print detection frame and recognition result
for line in result:
print(line)
# Visualization
from PIL import Image
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/korean.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
Visualization of results:
ppocr also supports direction classification. For more usage methods, please refer to: whl package instructions.
PaddleOCR supports using your own data for custom training or finetune, where the configuration file can refer to French model Modify the training data path, dictionary and other parameters.
For specific data preparation and training process, please refer to: Text Detection, Text Recognition, more functions such as predictive deployment, For functions such as data annotation, you can read the complete Document Tutorial.
In addition to installing the whl package for quick forecasting, ppocr also provides a variety of forecasting deployment methods. If necessary, you can read related documents:
The deployment tutorial uses the Chinese model by default. If you need to use other language models, please replace the model files and dictionaries yourself:
The detection model are as follows:
There are two detection models, namely the Chinese detection model and the English detection model. The Chinese detection model performs better on Chinese and English images. The English detection model is based on the Chinese model and is fine-tuned using English scene data and other language data finetune. Better in the English scene.
| model name | description | config | download |
|---|---|---|---|
| en_mobile_v2.0_det | The multi-language detection model | en_det_mv3_db.yml | inference model /trained model |
| ch_ppocr_mobile_v2.0_det | The chinese detection model | ch_det_mv3_db.yml | inference model / trained model |
The recognition model are as follows:
| model name | dict file | description | config | model size | download |
|---|---|---|---|---|---|
| french_mobile_v2.0_rec | ppocr/utils/dict/french_dict.txt | Lightweight model for French recognition | rec_french_lite_train.yml | 2.65M | inference model / trained model |
| german_mobile_v2.0_rec | ppocr/utils/dict/german_dict.txt | Lightweight model for German recognition | rec_german_lite_train.yml | 2.65M | inference model / trained model |
| korean_mobile_v2.0_rec | ppocr/utils/dict/korean_dict.txt | Lightweight model for Korean recognition | rec_korean_lite_train.yml | 3.9M | inference model / trained model |
| japan_mobile_v2.0_rec | ppocr/utils/dict/japan_dict.txt | Lightweight model for Japanese recognition | rec_japan_lite_train.yml | 4.23M | inference model / trained model |
| chinese_cht_mobile_v2.0_rec | ppocr/utils/dict/chinese_cht_dict.txt | Lightweight model for chinese cht recognition | rec_chinese_cht_lite_train.yml | 5.63M | inference model / trained model |
| te_mobile_v2.0_rec | ppocr/utils/dict/te_dict.txt | Lightweight model for Telugu recognition | rec_te_lite_train.yml | 2.63M | inference model / trained model |
| ka_mobile_v2.0_rec | ppocr/utils/dict/ka_dict.txt | Lightweight model for Kannada recognition | rec_ka_lite_train.yml | 2.63M | inference model / trained model |
| ta_mobile_v2.0_rec | ppocr/utils/dict/ta_dict.txt | Lightweight model for Tamil recognition | rec_ta_lite_train.yml | 2.63M | inference model / trained model |
| latin_mobile_v2.0_rec | ppocr/utils/dict/latin_dict.txt | Lightweight model for latin recognition | rec_latin_lite_train.yml | 2.6M | inference model / trained model |
| arabic_mobile_v2.0_rec | ppocr/utils/dict/arabic_dict.txt | Lightweight model for arabic recognition | rec_arabic_lite_train.yml | 2.6M | inference model / trained model |
| cyrillic_mobile_v2.0_rec | ppocr/utils/dict/cyrillic_dict.txt | Lightweight model for cyrillic recognition | rec_cyrillic_lite_train.yml | 2.6M | inference model / trained model |
| devanagari_mobile_v2.0_rec | ppocr/utils/dict/devanagari_dict.txt | Lightweight model for devanagari recognition | rec_devanagari_lite_train.yml | 2.6M | inference model / trained model |
| Language | Abbreviation | Language | Abbreviation | |
|---|---|---|---|---|
| chinese and english | ch | Arabic | ar | |
| english | en | Hindi | hi | |
| french | fr | Uyghur | ug | |
| german | german | Persian | fa | |
| japan | japan | Urdu | ur | |
| korean | korean | Serbian(latin) | rs_latin | |
| chinese traditional | ch_tra | Occitan | oc | |
| Italian | it | Marathi | mr | |
| Spanish | es | Nepali | ne | |
| Portuguese | pt | Serbian(cyrillic) | rs_cyrillic | |
| Russia | ru | Bulgarian | bg | |
| Ukranian | uk | Estonian | et | |
| Belarusian | be | Irish | ga | |
| Telugu | te | Croatian | hr | |
| Saudi Arabia | sa | Hungarian | hu | |
| Tamil | ta | Indonesian | id | |
| Afrikaans | af | Icelandic | is | |
| Azerbaijani | az | Kurdish | ku | |
| Bosnian | bs | Lithuanian | lt | |
| Czech | cs | Latvian | lv | |
| Welsh | cy | Maori | mi | |
| Danish | da | Malay | ms | |
| Maltese | mt | Adyghe | ady | |
| Dutch | nl | Kabardian | kbd | |
| Norwegian | no | Avar | ava | |
| Polish | pl | Dargwa | dar | |
| Romanian | ro | Ingush | inh | |
| Slovak | sk | Lak | lbe | |
| Slovenian | sl | Lezghian | lez | |
| Albanian | sq | Tabassaran | tab | |
| Swedish | sv | Bihari | bh | |
| Swahili | sw | Maithili | mai | |
| Tagalog | tl | Angika | ang | |
| Turkish | tr | Bhojpuri | bho | |
| Uzbek | uz | Magahi | mah | |
| Vietnamese | vi | Nagpur | sck | |
| Mongolian | mn | Newari | new | |
| Abaza | abq | Goan Konkani | gom |