PP-OCRv5

Install

PP-OCRv5 needs no extra beyond the base package.

pip install libreyolo

Predict

Weights download from Hugging Face on first use and are cached locally.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibrePPOCRl-ocr.pt")result = model(SAMPLE_IMAGE, save=True) for text, conf in zip(result.ocr.texts, result.ocr.conf):    print(text, float(conf))

libreyolo predict model=LibrePPOCRl-ocr.pt source=receipt.jpg save=True

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibrePPOCRl-ocr.pt")result = model(SAMPLE_IMAGE) # (N, 4, 2) polygons in reading order: top-left, top-right,# bottom-right, bottom-left. Detection quads are genuine polygons# (rotated text), so they populate result.ocr, not result.boxes.print(result.ocr.data.shape)print(result.ocr.det_conf)

Each checkpoint bundles both stages, detection and recognition, under one .pt file, with the recognition charset and pipeline defaults carried in the checkpoint metadata. The recognizer reads Simplified and Traditional Chinese, English, Japanese and pinyin with one dictionary. result.ocr is an OCRRegions payload: .data holds the four-point polygons, .texts the transcripts, .conf the per-region recognition score, and .det_conf the detection score. Multi-image sources run sequentially: the two-stage pipeline does not batch across images. See prediction for sources, streaming and result handling.

Variants

Two tiers: t, built on lighter PP-LCNetV3/PP-OCRv5_mobile backbones for CPU use, and l, built on PP-HGNetV2 server backbones for higher accuracy. Both tiers run detection at a fixed long-side limit and recognize crops in batches; rec_batch controls how many crops go through the recognizer per forward pass.

Validate

val() measures the pipeline against a directory of images plus a labels/<split>.jsonl file, or the equivalent dataset YAML, each label listing per-image text-region polygons and their transcripts. It reports detection hmean (IoU-matched precision/recall/F1), end-to-end F1 (hmean plus an exact transcript match after normalization, the checkpoint's fitness metric), and 1-NED, the mean normalized edit distance over matched pairs.

from libreyolo import LibreYOLO model = LibreYOLO("LibrePPOCRl-ocr.pt")metrics = model.val(data="my-dataset") print(metrics["metrics/det_hmean"])print(metrics["metrics/e2e_f1"])       # headline metricprint(metrics["metrics/rec_1-NED"])

libreyolo val model=LibrePPOCRl-ocr.pt data=my-dataset

Export

PP-OCRv5 is a two-network pipeline, detection and recognition moving together, not one traceable graph, and export is not implemented for it: no format is supported yet. Fine-tune the Apache-2.0 upstream training code directly and convert the result with weights/convert_ppocr_weights.py if you need a checkpoint outside this format.

Checkpoints

Every published weight file for this family.

Licensing

Citation

@misc{cui2025paddleocr30technicalreport,
      title={PaddleOCR 3.0 Technical Report}, 
      author={Cheng Cui and Ting Sun and Manhui Lin and Tingquan Gao and Yubo Zhang and Jiaxuan Liu and Xueqing Wang and Zelun Zhang and Changda Zhou and Hongen Liu and Yue Zhang and Wenyu Lv and Kui Huang and Yichao Zhang and Jing Zhang and Jun Zhang and Yi Liu and Dianhai Yu and Yanjun Ma},
      year={2025},
      eprint={2507.05595},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.05595}, 
}