ppocrv6-tiny-burnpack

Original models: https://huggingface.co/PaddlePaddle/PP-OCRv6_tiny_det_onnx and https://huggingface.co/PaddlePaddle/PP-OCRv6_tiny_rec_onnx Original authors: the PaddleOCR team at Baidu — PP-OCRv6 (2026), the tiny tier of a three-tier OCR family (1.5 M to 34.5 M parameters): a DBNet text detector (PPLCNetV4 backbone, RepLKFPN neck) and a CTC text recogniser (PPLCNetV4, 6 904-character dictionary, 49 languages) Converted by: Lucie666, using burn-onnx — format only

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This is not an original model, and no part of it is my work. It is a mechanical format conversion of PaddlePaddle/PP-OCRv6_tiny_det_onnx and PaddlePaddle/PP-OCRv6_tiny_rec_onnx — nothing was trained, fine-tuned, distilled, quantised or modified. No new weights were produced. All credit belongs to the authors above.

If you are looking for the models themselves, go to the two PaddlePaddle repositories above. This repository only exists so people running Burn don't each redo the conversion.

Note on metadata: Hugging Face's base_model_relation field only accepts adapter, merge, quantized or finetune. None describes a pure format conversion, so the field is deliberately left unset rather than filled with an inaccurate value — these weights are not quantised, they are the original f32 values.

The files det.bpk and rec.bpk hold the same weights as the upstream inference.onnx files, re-serialised into Burn's burnpack format so they can be loaded by a pure-Rust inference stack — no Python, no Paddle, no ONNX Runtime at inference time. dict.txt is the recogniser's character list, taken verbatim from the upstream inference.yml.

Why this exists

A complete, embeddable OCR in 6 MB: 1.7 MB of detector and 4.4 MB of recogniser, no tokenizer, Latin script with accents and CJK in the same dictionary. It is the OCR of rag3weaver (src/burn_ppocr.rs, feature burn-ocr): images go in, lines of text with boxes and confidences come out, on the same Burn/wgpu stack as its embedders and rerankers.

Provenance

PaddlePaddle/PP-OCRv6_tiny_det_onnx   inference.onnx   (opset 14, 1 780 590 bytes)
        │
        │  fix_onnx.py   (3 nodes: auto_pad SAME_UPPER → explicit pads, no weight touched)
        ▼
det_pads.onnx
        │
        │  burn-onnx 0.22.0-pre.1   (mechanical ONNX → Burn conversion, LoadStrategy::Bytes)
        ▼
det.bpk          weights, burnpack format
det.rs           model graph, generated Rust source (not distributed here)

PaddlePaddle/PP-OCRv6_tiny_rec_onnx   inference.onnx   (opset 11, 4 462 639 bytes)
        │                                               = rec_pads.onnx, byte for byte
        │  burn-onnx 0.22.0-pre.1
        ▼
rec.bpk          weights, burnpack format
rec.rs           model graph, generated Rust source (not distributed here)
        +
dict.txt         PostProcess.character_dict of rec_inference.yml, one entry per line

Nothing in this pipeline is hand-written, except the three-attribute patch below.

Why the detector needed a patch. Two Conv and one MaxPool (2×2 kernel, stride 1) in the detector are declared with auto_pad = SAME_UPPER. burn-onnx 0.22.0-pre.1 rejects that form when the input has dynamic dimensions (auto_pad SAME_UPPER/SAME_LOWER requires static input shape … Use explicit pads instead). fix_onnx.py rewrites those three nodes as pads = [0, 0, 1, 1] — the exact padding SAME_UPPER means for a 2×2/stride-1 kernel (total k − 1 = 1, placed bottom/right). Same arithmetic, same weights, and height/width stay dynamic. The recogniser needed nothing: rec_pads.onnx is the upstream file unchanged (same sha256).

A caveat before you regenerate. Burnpack serialisation is not byte-deterministic: two builds from the same ONNX produce files of identical size but different bytes. The tensor values are unaffected. The checksums below verify this download, not a reproduction.

Reproducing it

pip install onnx                      # only `onnx`, no runtime
python fix_onnx.py inference.onnx det_pads.onnx pads     # detector only

// build.rs
use burn_onnx::{LoadStrategy, ModelGen};

fn main() {
    ModelGen::new()
        .input("det_pads.onnx")           // patched PaddlePaddle/PP-OCRv6_tiny_det_onnx
        .out_dir("model/")
        .load_strategy(LoadStrategy::Bytes)
        .run_from_script();
    ModelGen::new()
        .input("rec_pads.onnx")           // = PaddlePaddle/PP-OCRv6_tiny_rec_onnx inference.onnx
        .out_dir("model/")
        .load_strategy(LoadStrategy::Bytes)
        .run_from_script();
}

Both models are far under the 2 GB ONNX protobuf limit. 0.22.0-pre.1 is used for consistency with a burn 0.22.0-pre.2 runtime: generated code and runtime must match versions. The generated sources carry the absolute path of the input ONNX in their first line; rag3weaver replaces it with the upstream repository name.

Verifying the download

det.bpk          sha256  73a139fa82b9fc8f7c03b66ab3c3dc9e959e8c1f4d95b2da09b4e50529e76b04
                 size    1 737 476 bytes  (1.66 MiB)
rec.bpk          sha256  53bfcb22a068cc6991f2b8b3ba0782a1aac3c54c16895ae7138eb4e755169436
                 size    4 443 368 bytes  (4.24 MiB)
dict.txt         sha256  c5cbe34ef40c29c4df07ed012bf96569cb69a2d2a01a07027e9f13cb832bd9cd
                 size    27 156 bytes  (6 904 lines, UTF-8)
det_pads.onnx    sha256  f74ec758df06b1f77cde82a44bc840cbb39f8b7cf1573f373ea052a1e8d93ae6
                 size    1 780 566 bytes
rec_pads.onnx    sha256  9ef676d6ed3c88256a2d92c640c44f25b0c40947e111b14b8be8f594091563e6
                 size    4 462 639 bytes  (identical to upstream inference.onnx)

SHA256SUMS covers every file in the repository except this card; sha256sum -c SHA256SUMS checks them all.

Model interface

Both graphs are backend-agnostic Burn modules (burn 0.22: Tensor<D> on the crate's default backend, Model::from_bytes(bytes, &device) loads a burnpack).

// det.bpk — PP-OCRv6_tiny_det
pub fn forward(&self, x: Tensor<4>) -> Tensor<4>   // [B, 3, H, W] → [B, 1, H, W], sigmoid
// rec.bpk — PP-OCRv6_tiny_rec
pub fn forward(&self, x: Tensor<4>) -> Tensor<3>   // [B, 3, 48, W] → [B, W/8, 6906], softmax

B, H, W are dynamic at run time (checked on 320×320, 224×416 and 2464×736 for the detector; W = 160/320/640 and B = 1/2 for the recogniser). The recogniser's 6 906 classes are: index 0 = CTC blank, 1..=6904 = line i of dict.txt, 6905 = space.

Pre- and post-processing

Not included in the graphs — this is what PaddleOCR does around them (from the upstream inference.yml files, shipped here as det_inference.yml / rec_inference.yml, and PaddleOCR's operators.py, predict_rec.py, db_postprocess.py, rec_postprocess.py):

• Detector. Resize so the shorter side is at least 736 (limit_type min, cap 4000 on the longer side), then round height and width to a multiple of 32. Normalise x/255 with mean [0.485, 0.456, 0.406], std [0.229, 0.224, 0.225] applied to the channels in BGR order (PaddleOCR decodes with OpenCV and never swaps). CHW. Post-processing (DBPostProcess): binarise at thresh 0.2, contours → minimum-area boxes, drop sides < 3 px, score = mean probability inside the box ≥ box_thresh 0.4, unclip by d = area × 1.4 / perimeter, drop sides < 5 px, max_candidates 3000, scale back to the source image.
• Recogniser. Crop each box (rotate 90° if height/width ≥ 1.5), resize to height 48 and width ceil(48 · w/h), capped at 48 × max(320/48, max w/h of the batch); (x/255 − 0.5) / 0.5 in BGR, zero-pad on the right, batches of 6 sorted by aspect ratio. Greedy CTC: argmax per step, merge consecutive repeats, drop blanks; confidence = mean probability of the kept characters.

rag3weaver's src/burn_ppocr.rs reimplements all of this in Rust (with axis-aligned boxes instead of minAreaRect).

Numerical parity

Checked against onnxruntime 1.29 (CPU) running the very same ONNX files, fed with the same pre-processed input tensors (so only the networks are compared), on a 400×120 test image with two lines of text (detector input [1, 3, 736, 2464], recogniser input [2, 3, 48, 320]):

                          max|Δ|     mean|Δ|     values with |Δ| > 1e-3
det map  [1, 1, 736, 2464]   1.81e-3    1.77e-6     87 of 1 813 504
rec probs [2, 40, 6906]      1.44e-5    9.2e-11     0

The 87 detector pixels sit on glyph edges, where the sigmoid is steep on a 6× upscaled image. Burn's ndarray backend shows the same gap against onnxruntime (1.89e-3) while wgpu and ndarray agree to 9.7e-5: it is f32 accumulation noise from a different operation order, not a backend defect. Boxes are identical, and both stacks decode the same text with the same confidences — "Hello rag3weaver" (0.987) and "OCR 2026" (0.984). ppocr_ref.py is the oracle script.

Runtime backend for this check: Burn + wgpu/Vulkan on an AMD Radeon AI PRO R9700 (Navi 48, RDNA4, gfx1201) via RADV.

License

Apache-2.0, inherited from PaddlePaddle/PP-OCRv6_tiny_det_onnx and PaddlePaddle/PP-OCRv6_tiny_rec_onnx (PaddleOCR, Apache-2.0). All credit for the models belongs to their authors:

@misc{zhang2026ppocrv6,
  title={PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks},
  author={Yubo Zhang and Xueqing Wang and Manhui Lin and Yue Zhang and Penglongyi Deng and Ting Sun and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Changda Zhou and Hongen Liu and Suyin Liang and Cheng Cui and Yi Liu and Dianhai Yu and Yanjun Ma},
  year={2026},
  eprint={2606.13108},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2606.13108},
}

@misc{paddleocr,
  title={PaddleOCR: Awesome multilingual OCR and Document Parsing toolkits based on PaddlePaddle},
  author={{PaddlePaddle Authors}},
  howpublished={\url{https://github.com/PaddlePaddle/PaddleOCR}},
  year={2020}
}