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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
3 Custom Training
4 Supported languages and abbreviations

1 Installation

1.1 paddle installation

# cpu
pip install paddlepaddle

# gpu
pip instll paddlepaddle-gpu

1.2 paddleocr package installation

pip install

pip install "paddleocr>=2.0.4" # 2.0.4 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/japan_2.jpg --lang=japan

The result is a list, each item contains a text box, text and recognition confidence

[[[671.0, 60.0], [847.0, 63.0], [847.0, 104.0], [671.0, 102.0]], ('もちもち', 0.9993342)]
[[[394.0, 82.0], [536.0, 77.0], [538.0, 127.0], [396.0, 132.0]], ('自然の', 0.9919842)]
[[[880.0, 89.0], [1014.0, 93.0], [1013.0, 127.0], [879.0, 124.0]], ('とろっと', 0.9976762)]
[[[1067.0, 101.0], [1294.0, 101.0], [1294.0, 138.0], [1067.0, 138.0]], ('后味のよい', 0.9988712)]
......

Recognition

paddleocr --image_dir doc/imgs_words/japan/1.jpg --det false --lang=japan

The result is a tuple, which returns the recognition result and recognition confidence

('したがって', 0.99965394)

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)
# 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/korean.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')

Visualization of results:

Recognition

from paddleocr import PaddleOCR
ocr = PaddleOCR(lang="german")
img_path ='PaddleOCR/doc/imgs_words/german/1.jpg'
result = ocr.ocr(img_path, det=False, cls=True)
for line in result:
    print(line)

The result is a tuple, which only contains the recognition result and recognition confidence

('leider auch jetzt', 0.97538936)

Detection

from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path ='PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path, rec=False)
for line in result:
    print(line)

# show result
from PIL import Image

image = Image.open(img_path).convert('RGB')
im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')

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]]
......

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 Support languages and abbreviations

Language	Abbreviation
chinese and english	ch
english	en
french	fr
german	german
japan	japan
korean	korean
chinese traditional	ch_tra
Italian	it
Spanish	es
Portuguese	pt
Russia	ru
Arabic	ar
Hindi	hi
Uyghur	ug
Persian	fa
Urdu	ur
Serbian(latin)	rs_latin
Occitan	oc
Marathi	mr
Nepali	ne
Serbian(cyrillic)	rs_cyrillic
Bulgarian	bg
Ukranian	uk
Belarusian	be
Telugu	te
Kannada	kn
Tamil	ta
Afrikaans	af
Azerbaijani	az
Bosnian	bs
Czech	cs
Welsh	cy
Danish	da
Estonian	et
Irish	ga
Croatian	hr
Hungarian	hu
Indonesian	id
Icelandic	is
Kurdish	ku
Lithuanian	lt
Latvian	lv
Maori	mi
Malay	ms
Maltese	mt
Dutch	nl
Norwegian	no
Polish	pl
Romanian	ro
Slovak	sk
Slovenian	sl
Albanian	sq
Swedish	sv
Swahili	sw
Tagalog	tl
Turkish	tr
Uzbek	uz
Vietnamese	vi
Mongolian	mn
Abaza	abq
Adyghe	ady
Kabardian	kbd
Avar	ava
Dargwa	dar
Ingush	inh
Lak	lbe
Lezghian	lez
Tabassaran	tab
Bihari	bh
Maithili	mai
Angika	ang
Bhojpuri	bho
Magahi	mah
Nagpur	sck
Newari	new
Goan Konkani	gom
Saudi Arabia	sa

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