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.pt bundling the DB text detector (det.*) and the
CTC text recognizer (rec.*) plus the PP-OCRv5 recognition dictionary as
charset metadata. Recognition covers Simplified Chinese, Traditional
Chinese, English, Japanese, and Chinese pinyin with one dictionary and one
model.1from libreyolo import LibreYOLO
2
3model = LibreYOLO("LibrePPOCRl-ocr.pt")
4r = model("receipt.jpg")
5for poly, text, conf in zip(r.ocr.polygons, r.ocr.texts, r.ocr.conf):
6 print(text, float(conf))weights/convert_ppocr_weights.py from the official Apache-2.0
PP-OCRv5 training checkpoints released by the
PaddleOCR project
(paper: PaddleOCR 3.0 Technical Report):| Upstream checkpoint | SHA-256 |
|---|---|
PP-OCRv5_server_det_pretrained.pdparams | 2802f7d4748ea592819ae4550c195c5bdb43755dfdb5ebd25e01bb4d885aebc9 |
PP-OCRv5_server_rec_pretrained.pdparams | 8ce5dfc1294af6ee680d562841a9909257d6a9a9242387c2e8dc50ea8f647143 |
LibrePPOCRl-ocr.pt, SHA-256
6a58b6a2af947a40d48e50c2aa2f050b300d10367bdba5840993b900bf59358e.det./rec. namespacing); learned parameters are
unchanged. Stage parity vs the official PP-OCRv5 inference graphs on
identical input tensors: detection maps match to <= 1e-4 and recognition
probabilities to <= 6e-5 with identical argmax.