--- title: ppocr-mlx canonical_url: "https://www.modelscope.cn/models/jasonni/ppocr-mlx" md_url: "https://www.modelscope.cn/models/jasonni/ppocr-mlx.md" repository: jasonni/ppocr-mlx last_updated: 2026-06-19 license: apache-2.0 parameters: 1.1B tensor_type: - F32 - F16 - I64 library_name: - mlx - safetensors language: - en - zh - ja - ko downloads: 236 stars: 0 tags: - mlx - mlx-weights - paddlepaddle-ocr - ppocrv5 - ppocrv6 - ppdoclayoutv3 - pp-structure - apple-silicon --- # ppocr-mlx > ppocr-mlx - jasonni 在 ModelScope 开源的模型。This repository is an MLX conversion of the PaddlePaddle PP-Structure and PP-OCR model families. Every subdirectory holds a converted MLX weights file (model.mlx.safetensors) alongside the original source weights and… jasonni/ppocr-mlx 是 ModelScope 魔搭社区上的 1.1B 参数机器学习模型，采用 apache-2.0 许可。 - **Repository**: jasonni/ppocr-mlx - **License**: apache-2.0 - **Parameters**: 1.1B - **Tags**: mlx, mlx-weights, paddlepaddle-ocr, ppocrv5, ppocrv6, ppdoclayoutv3, pp-structure, apple-silicon - **Downloads**: 236 - **Stars**: 0 - **Last updated**: 2026-06-19 Source: https://www.modelscope.cn/models/jasonni/ppocr-mlx --- # PP-Structure / PP-OCR Models — MLX This repository is an **[MLX](https://github.com/ml-explore/mlx)** conversion of the PaddlePaddle **PP-Structure** and **PP-OCR** model families. Every subdirectory holds a converted MLX weights file (`model.mlx.safetensors`) alongside the original source weights and configs, so the models can run natively and efficiently on Apple Silicon (M-series). The models are converted from the official PaddlePaddle / Hugging Face [`transformers`](https://github.com/huggingface/transformers) safetensors checkpoints. They cover the full document-intelligence pipeline: layout analysis, text detection & recognition, orientation & rectification, table recognition, and formula recognition. > See each subdirectory's own `README.md` for model-specific details, accuracy metrics, and > the original PaddlePaddle usage examples. ## Repository layout Models are grouped by pipeline stage. Below, `↳` links each folder to its source model. ### Document layout analysis | Folder | Model | Description | | --- | --- | --- | | [`doclayoutv3/`](./doclayoutv3) | PP-DocLayoutV3 | RT-DETR-style detector (HGNetV2-L backbone) for 25 document layout regions (title, text, figure, table, formula, …). | ### Text detection (PP-OCRv5 / v6) | Folder | Model | Description | | --- | --- | --- | | [`det/`](./det) | PP-OCRv5_mobile_det | Legacy mobile text-line detector (LCNetV3 backbone, scale 0.75). | | [`det_v6_medium/`](./det_v6_medium) | PP-OCRv6_medium_det | Largest v6 detector — LCNetV4 backbone + RepLKFPN neck, 15.5M params. | | [`det_v6_small/`](./det_v6_small) | PP-OCRv6_small_det | Mid-tier v6 detector, 2.48M params. | | [`det_v6_tiny/`](./det_v6_tiny) | PP-OCRv6_tiny_det | Smallest v6 detector, 0.43M params. | ### Text recognition (PP-OCRv5 / v6) | Folder | Model | Description | | --- | --- | --- | | [`rec/`](./rec) | PP-OCRv5_mobile_rec | Legacy mobile recognizer (LCNetV3 backbone). | | [`en_rec/`](./en_rec) | PP-OCRv5_mobile_rec (EN) | English-dictionary variant of the mobile recognizer. | | [`server_rec/`](./server_rec) | PP-OCRv5_server_rec | Server-grade recognizer for ZH/EN/JA + handwriting, vertical text, pinyin, rare characters. | | [`rec_v6_medium/`](./rec_v6_medium) | PP-OCRv6_medium_rec | Largest v6 recognizer — LCNetV4 + EncoderWithLightSVTR, CTC+NRTR heads, 50 languages, 19M params. | | [`rec_v6_small/`](./rec_v6_small) | PP-OCRv6_small_rec | Mid-tier v6 recognizer, 5.2M params, 50 languages. | | [`rec_v6_tiny/`](./rec_v6_tiny) | PP-OCRv6_tiny_rec | Smallest v6 recognizer, 1.1M params, 49 languages. | ### Orientation & rectification | Folder | Model | Description | | --- | --- | --- | | [`ori/`](./ori) | PP-LCNet_x1_0_doc_ori | Document image orientation classifier (0°/90°/180°/270°), 99.06% avg accuracy. | | [`uvdoc/`](./uvdoc) | UVDoc | Document image unwarping / geometric rectification (CER 0.179 on DocUNet benchmark). | ### Table recognition | Folder | Model | Description | | --- | --- | --- | | [`table_cls/`](./table_cls) | PP-LCNet_x1_0_table_cls | Wired vs. wireless table classifier, 94.2% Top-1. | | [`table_structure/`](./table_structure) | SLANet | Legacy table-structure recognition (LCNet backbone, scale 1). | | [`table_wired/`](./table_wired) | SLANeXt_wired | Wired-table structure recognition, 69.65% accuracy, 351M. | | [`table_wireless/`](./table_wireless) | SLANeXt_wireless | Wireless-table structure recognition, 69.65% accuracy, 351M. | | [`table_cell_wired/`](./table_cell_wired) | RT-DETR-L_wired_table_cell_det | Wired-table cell detector (RT-DETR-L), 82.7% Top-1, 124M. | | [`table_cell_wireless/`](./table_cell_wireless) | RT-DETR-L_wireless_table_cell_det | Wireless-table cell detector (RT-DETR-L), 82.7% Top-1, 124M. | ### Formula recognition | Folder | Model | Description | | --- | --- | --- | | [`formula/`](./formula) | PP-FormulaNet_plus-L | Encoder-decoder vision-language model that converts formula images to LaTeX (~182M params, 50k-token vocabulary). | ## Pipeline These modules compose into the standard PP-Structure document pipeline: ``` ┌────────────┐ page image → │ doc ori │ (optional) orient the page └─────┬──────┘ ┌─────▼──────┐ │ uvdoc │ (optional) dewarp the page └─────┬──────┘ ┌─────▼──────┐ │ doclayoutv3│ detect layout regions └─────┬──────┘ ┌─────────┼──────────┐ ▼ ▼ ▼ text branch table formula ┌──────┐ ┌───────┐ ┌───────┐ │ det │ │ cls │ │formula│ └──┬───┘ └───┬───┘ └───────┘ │ ┌───┴──────┐ ▼ ▼ ▼ ┌────┐ cell det structure │rec │ (wired/ (wired/ └────┘ wireless) wireless) ``` For the OCR sub-pipeline, PP-OCRv6 pairs `det_v6_*` with the matching `rec_v6_*` tier (e.g. `det_v6_medium` + `rec_v6_medium`), selectable across medium / small / tiny for server-to-edge trade-offs. ## Loading the MLX weights Each folder follows the same convention — the MLX weights live in `model.mlx.safetensors` and the architecture in `config.json`: ``` / ├── model.mlx.safetensors # MLX-converted weights (load with mlx.nn / mlx-vlm) ├── model.safetensors # original source weights ├── config.json # architecture config └── preprocessor_config.json (or processor_config.json) ``` Load with MLX (Python): ```python import mlx.core as mx from mlx.utils import tree_unflatten weights = mx.load("det_v6_medium/model.mlx.safetensors") params = tree_unflatten(list(weights.items())) ``` > These are weight conversions only. A matching MLX model implementation (e.g. via > [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) or a custom MLX module) is required to run > inference. Refer to each subdirectory's `config.json` for the exact architecture. ## Model sources Original checkpoints and documentation from the [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) project and the [PaddlePaddle](https://huggingface.co/PaddlePaddle) Hugging Face organization. ## License Apache 2.0. See the [LICENSE](./LICENSE) of the upstream PaddleOCR project for details.