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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
- 1.1 paddle installation
- 1.2 paddleocr package installation
2 Quick Use
- 2.1 Command line operation
- 2.2 python script running
3 Custom Training
4 Inference and Deployment
4 Supported languages and abbreviations

1 Installation

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] (#language_abbreviations) 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

ppocr 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='/path/to/PaddleOCR/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

ppocr supports using your own data for custom training or finetune, where the recognition model can refer to French configuration file 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:

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

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