Multi-language model

Recent Update

• 2022.5.8 update the PP-OCRv3 version of the multi-language detection and recognition model, and the average recognition accuracy has increased by more than 5%.
• 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

# cpu
pip install paddlepaddle

# gpu
pip install paddlepaddle-gpu

1.2 PaddleOCR package installation

pip install paddleocr

Build and install locally

python3 -m build
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 specified by the --lang parameter. The supported languages are listed 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 2-tuple, which contains 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 represents the coordinates of a text box.

[[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 Run with Python script

PPOCR is able to run with Python scripts for easy integration with 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 detailed usage, please refer to: whl package instructions.

3 Custom training

PPOCR supports using your own data for custom training or fine-tune, 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:

• Python Inference
• C++ Inference
• Serving
• Mobile
• Benchmark

5 Support languages and abbreviations

Language	Abbreviation		Language	Abbreviation
Chinese & 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	chinese_cht		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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