1. Introduction to PP-OCRv5 Multilingual Text Recognition

PP-OCRv5 is the latest generation text recognition solution in the PP-OCR series, focusing on multi-scenario and multilingual text recognition tasks. In terms of supported text types, the default configuration of the recognition model can accurately identify five major types: Simplified Chinese, Pinyin, Traditional Chinese, English, and Japanese. Additionally, PP-OCRv5 offers multilingual text recognition capabilities covering 106 languages, including Korean, Spanish, French, Portuguese, German, Italian, Russian, Thai, Greek and more (for a full list of supported languages and abbreviations, see Section 4). Compared to the previous PP-OCRv3 version, PP-OCRv5 achieves over a 30% improvement in accuracy for multilingual text recognition.

French Recognition Result

German Recognition Result

Korean Recognition Result

Russian Recognition Result

Thai recognition result

Greek recognition result

Arabic recognition Result

Hindi recognition Result

Tamil recognition Result

Telugu recognition Result

2. Quick Start

You can specify the language for text recognition by using the --lang parameter when running the general OCR pipeline in the command line:

# Use the `--lang` parameter to specify the French recognition model
paddleocr ocr -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_french01.png \
    --lang fr \
    --use_doc_orientation_classify False \
    --use_doc_unwarping False \
    --use_textline_orientation False \
    --save_path ./output \
    --device gpu:0 

For explanations of the other command-line parameters, please refer to the Command Line Usage section of the general OCR pipeline documentation. After running, the results will be displayed in the terminal:

{'res': {'input_path': '/root/.paddlex/predict_input/general_ocr_french01.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': True, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': None, 'page_index': None, 'model_settings': {'use_doc_orientation_classify': False, 'use_doc_unwarping': False}, 'angle': -1}, 'dt_polys': array([[[119,  23],
        ...,
        [118,  75]],

       ...,

       [[109, 506],
        ...,
        [108, 556]]], dtype=int16), 'text_det_params': {'limit_side_len': 64, 'limit_type': 'min', 'thresh': 0.3, 'max_side_limit': 4000, 'box_thresh': 0.6, 'unclip_ratio': 1.5}, 'text_type': 'general', 'textline_orientation_angles': array([-1, ..., -1]), 'text_rec_score_thresh': 0.0, 'rec_texts': ['mifere; la profpérité & les fuccès ac-', 'compagnent l’homme induftrieux.', 'Quel eft celui qui a acquis des ri-', 'cheffes, qui eft devenu puiffant, qui', 's’eft couvert de gloire, dont l’éloge', 'retentit par-tout, qui fiege au confeil', "du Roi? C'eft celui qui bannit la pa-", "reffe de fa maifon, & qui a dit à l'oifi-", 'veté : tu es mon ennemie.'], 'rec_scores': array([0.98409832, ..., 0.98091048]), 'rec_polys': array([[[119,  23],
        ...,
        [118,  75]],

       ...,

       [[109, 506],
        ...,
        [108, 556]]], dtype=int16), 'rec_boxes': array([[118, ...,  81],
       ...,
       [108, ..., 562]], dtype=int16)}}

If you specify save_path, the visualization results will be saved to the specified path. An example of the visualized result is shown below:

You can also use Python code to specify the recognition model for a particular language when initializing the general OCR pipeline via the lang parameter:

from paddleocr import PaddleOCR

ocr = PaddleOCR(
    lang="fr", # Specify French recognition model with the lang parameter
    use_doc_orientation_classify=False, # Disable document orientation classification model
    use_doc_unwarping=False, # Disable text image unwarping model
    use_textline_orientation=False, # Disable text line orientation classification model
)
result = ocr.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_french01.png")
for res in result:
    res.print()
    res.save_to_img("output")
    res.save_to_json("output")

For more details on the PaddleOCR class parameters, please refer to the Python Scripting Integration section of the general OCR pipeline documentation.

3. Performance Comparison

Model	Download Link	Accuracy on the corresponding dataset (%)	Improvement over the previous generation model (%) ｜
korean_PP-OCRv5_mobile_rec	Inference Model/Pretrained Model	88.0	65.0
latin_PP-OCRv5_mobile_rec	Inference Model/Pretrained Model	84.7	46.8
eslav_PP-OCRv5_mobile_rec	Inference Model/Pretrained Model	81.6	31.4
th_PP-OCRv5_mobile_rec	Inference Model/Pretrained Model	82.68	-
el_PP-OCRv5_mobile_rec	Inference Model/Pretrained Model	89.28	-
en_PP-OCRv5_mobile_rec	Inference Model/Pretrained Model	85.25	11.0
cyrillic_PP-OCRv5_mobile_rec	Inference Model/Trained Model	80.27	21.2
arabic_PP-OCRv5_mobile_rec	Inference Model/Trained Model	81.27	22.83
devanagari_PP-OCRv5_mobile_rec	Inference Model/Trained Model	84.96	68.26
te_PP-OCRv5_mobile_rec	Inference Model/Trained Model	87.65	43.47
ta_PP-OCRv5_mobile_rec	Inference Model/Trained Model	94.2	39.23

Notes: - Korean Dataset: The latest PP-OCRv5 dataset containing 5,007 Korean text images. - Latin Script Language Dataset: The latest PP-OCRv5 dataset containing 3,111 images of Latin script languages. - East Slavic Language Dataset: The latest PP-OCRv5 dataset containing a total of 7,031 text images in Russian, Belarusian, and Ukrainian. - Thai dataset: The latest PP-OCRv5 constructed Thai dataset contains a total of 4,261 text images for recognition. - Greek dataset: The latest PP-OCRv5 constructed Greek dataset contains a total of 2,799 text images for recognition. - English dataset: The latest PP-OCRv5 constructed English dataset contains a total of 6,530 text images for recognition. - Cyrillic Dataset: The latest PP-OCRv5 Cyrillic recognition dataset contains a total of 7,600 text images. - Tamil Dataset: The latest PP-OCRv5 Tamil recognition dataset contains a total of 2,121 text images. - Telugu Dataset: The latest PP-OCRv5 Telugu recognition dataset contains a total of 2,478 text images. - Arabic Dataset: The latest PP-OCRv5 Arabic, Sanskrit, etc. recognition dataset contains a total of 2,676 text images. - Devanagari Dataset: The latest PP-OCRv5 Devanagari recognition dataset contains a total of 3,611 text images.

4. Supported Languages and Abbreviations

Language	Description	Abbreviation		Language	Description	Abbreviation
Chinese	Chinese & English	ch		Hungarian	Hungarian	hu
English	English	en		Serbian (latin)	Serbian (latin)	rs_latin
French	French	fr		Indonesian	Indonesian	id
German	German	de		Occitan	Occitan	oc
Japanese	Japanese	japan		Icelandic	Icelandic	is
Korean	Korean	korean		Lithuanian	Lithuanian	lt
Traditional Chinese	Chinese Traditional	chinese_cht		Maori	Maori	mi
Afrikaans	Afrikaans	af		Malay	Malay	ms
Italian	Italian	it		Dutch	Dutch	nl
Spanish	Spanish	es		Norwegian	Norwegian	no
Bosnian	Bosnian	bs		Polish	Polish	pl
Portuguese	Portuguese	pt		Slovak	Slovak	sk
Czech	Czech	cs		Slovenian	Slovenian	sl
Welsh	Welsh	cy		Albanian	Albanian	sq
Danish	Danish	da		Swedish	Swedish	sv
Estonian	Estonian	et		Swahili	Swahili	sw
Irish	Irish	ga		Tagalog	Tagalog	tl
Croatian	Croatian	hr		Turkish	Turkish	tr
Uzbek	Uzbek	uz		Latin	Latin	la
Russian	Russian	ru		Belarusian	Belarusian	be
Ukrainian	Ukrainian	uk		Thai	Thai	th
Greek	Greek	el		Azerbaijani	Azerbaijani	az
Kurdish	Kurdish	ku		Latvian	Latvian	lv
Maltese	Maltese	mt		Pali	Pali	pi
Romanian	Romanian	ro		Vietnamese	Vietnamese	vi
Finnish	Finnish	fi		Basque	Basque	eu
Galician	Galician	gl		Luxembourgish	Luxembourgish	lb
Romansh	Romansh	rm		Catalan	Catalan	ca
Quechua	Quechua	qu		Telugu	Telugu	te
Serbian (Cyrillic)	Serbian (Cyrillic)	sr		Bulgarian	Bulgarian	bg
Mongolian	Mongolian	mn		Abkhaz	Abkhaz	ab
Adyghe	Adyghe	ady		Kabardian	Kabardian	kbd
Avar	Avar	av		Dargwa	Dargwa	dar
Ingush	Ingush	inh		Chechen	Chechen	ce
Lak	Lak	lki		Lezgian	Lezgian	lez
Tabasaran	Tabasaran	tab		Kazakh	Kazakh	kk
Kyrgyz	Kyrgyz	ky		Tajik	Tajik	tg
Macedonian	Macedonian	mk		Tatar	Tatar	tt
Chuvash	Chuvash	cv		Bashkir	Bashkir	ba
Mari	Mari	mhr		Moldovan	Moldovan	mo
Udmurt	Udmurt	udm		Komi	Komi	kv
Ossetian	Ossetian	os		Buriat	Buriat	bua
Kalmyk	Kalmyk	xal		Tuvinian	Tuvinian	tyv
Sakha	Sakha	sah		Karakalpak	Karakalpak	kaa
Arabic	Arabic	ar		Persian	Persian	fa
Uyghur	Uyghur	ug		Urdu	Urdu	ur
Pashto	Pashto	ps		Kurdish	Kurdish	ku
Sindhi	Sindhi	sd		Balochi	Balochi	bal
Hindi	Hindi	hi		Marathi	Marathi	mr
Nepali	Nepali	ne		Bihari	Bihari	bh
Maithili	Maithili	mai		Old English	Old English	ang
Bhojpuri	Bhojpuri	bho		Magahi	Magahi	mah
Sadri	Sadri	sck		Newar	Newar	new
Konkani	Konkani	gom		Sanskrit	Sanskrit	sa
Haryanvi	Haryanvi	bgc		Tamil	Tamil	ta

5. Models and Their Supported Languages

Model	Supported Languages
korean_PP-OCRv5_mobile_rec	Korean, English
latin_PP-OCRv5_mobile_rec	French, German, Afrikaans, Italian, Spanish, Bosnian, Portuguese, Czech, Welsh, Danish, Estonian, Irish, Croatian, Uzbek, Hungarian, Serbian (Latin), Indonesian, Occitan, Icelandic, Lithuanian, Maori, Malay, Dutch, Norwegian, Polish, Slovak, Slovenian, Albanian, Swedish, Swahili, Tagalog, Turkish, Latin, Azerbaijani, Kurdish, Latvian, Maltese, Pali, Romanian, Vietnamese, Finnish, Basque, Galician, Luxembourgish, Romansh, Catalan, Quechua
eslav_PP-OCRv5_mobile_rec	Russian, Belarusian, Ukrainian, English
th_PP-OCRv5_mobile_rec	Thai, English
el_PP-OCRv5_mobile_rec	Greek, English
en_PP-OCRv5_mobile_rec	English
cyrillic_PP-OCRv5_mobile_rec	Russian, Belarusian, Ukrainian, Serbian (Cyrillic), Bulgarian, Mongolian, Abkhazian, Adyghe, Kabardian, Avar, Dargin, Ingush, Chechen, Lak, Lezgin, Tabasaran, Kazakh, Kyrgyz, Tajik, Macedonian, Tatar, Chuvash, Bashkir, Malian, Moldovan, Udmurt, Komi, Ossetian, Buryat, Kalmyk, Tuvan, Sakha, Karakalpak, English
arabic_PP-OCRv5_mobile_rec	Arabic, Persian, Uyghur, Urdu, Pashto, Kurdish, Sindhi, Balochi, English
devanagari_PP-OCRv5_mobile_rec	Hindi, Marathi, Nepali, Bihari, Maithili, Angika, Bhojpuri, Magahi, Santali, Newari, Konkani, Sanskrit, Haryanvi, English
ta_PP-OCRv5_mobile_rec	Tamil, English
te_PP-OCRv5_mobile_rec	Telugu, English

note: en_PP-OCRv5_mobile_rec is an optimized version based on the PP-OCRv5 model, specifically fine-tuned for English scenarios. It demonstrates higher recognition accuracy and better adaptability when processing English text.

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