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Multi-language model

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.

1 Installation

PaddleOCR provides two installation methods

1.1 paddle installation

# cpu
pip install paddlepaddle

# gpu
pip install paddlepaddle-gpu

1.2 paddleocr package installation

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

2 Quick Use

2.1 Command Line Operation

  • 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]]
......

2.2 Python Script Running

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.

3 Custom Training

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.

4 Inference and Deployment

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:

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

5 Support languages and abbreviations

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