1. Introduction to PP-OCRv5 Multilingual Text Recognition

PP-OCRv5 is the latest generation of the PP-OCR series text recognition solutions, focusing on text recognition tasks across multiple scenarios and languages. By default, the recognition model supports accurate recognition of five mainstream text types: Simplified Chinese, Chinese Pinyin, Traditional Chinese, English, and Japanese. In addition, PP-OCRv5 provides multilingual recognition capabilities covering 37 languages, including Korean, Spanish, French, Portuguese, German, Italian, Russian, and more (see Section 4 for the full list of supported languages and abbreviations). Compared to the previous PP-OCRv3 version, PP-OCRv5 achieves more than a 30% improvement in recognition accuracy for multilingual tasks.

2. Quick Start

You can use the --lang parameter in the command line to specify the text recognition model for your target language when running the general OCR pipeline:

# 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 other command line parameters, please refer to the command line usage of the general OCR pipeline. After execution, results will be printed to 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 in the save_path directory. An example visualization is shown below:

You can also use Python code to specify the recognition model for your target language using the lang parameter when initializing the general OCR pipeline:

from paddleocr import PaddleOCR

ocr = PaddleOCR(
    lang="fr", # Specify the French recognition model via the lang parameter
    use_doc_orientation_classify=False, # Disable document orientation classification
    use_doc_unwarping=False, # Disable text image unwarping
    use_textline_orientation=False, # Disable textline orientation classification
)
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, refer to the Python script integration of the general OCR pipeline.

3. Benchmark Comparison

Model	Korean Dataset Accuracy (%)		Model	Latin Script Languages Dataset Accuracy (%)		Model	East Slavic Languages Dataset Accuracy (%)
korean_PP-OCRv5_mobile_rec	88.0		latin_PP-OCRv5_mobile_rec	84.7		eslav_PP-OCRv5_mobile_rec	85.8
korean_PP-OCRv3_mobile_rec	23.0		latin_PP-OCRv3_mobile_rec	37.9		cyrillic_PP-OCRv3_mobile_rec	50.2

Notes: - Korean Dataset: PP-OCRv5's latest dataset containing 5,007 Korean text images. - Latin Script Languages Dataset: The latest PP-OCRv5 recognition dataset, containing 3,111 text images in Latin script languages. - East Slavic Languages Dataset: PP-OCRv5's latest dataset containing a total of 7,031 Russian, Belarusian, and Ukrainian 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)	rslatin
French	French	fr		Indonesian	Indonesian	id
German	German	de		Occitan	Occitan	oc
Japanese	Japanese	japan		Icelandic	Icelandic	is
Korean	Korean	korean		Lithuanian	Lithuanian	lt
Chinese Traditional	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	Ukranian	uk

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