Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+- zh
+- ja
+- ko
+pipeline_tag: image-to-text
+tags:
+- mlx
+- mlx-weights
+- paddlepaddle-ocr
+- ppocrv5
+- ppocrv6
+- ppdoclayoutv3
+- pp-structure
+- apple-silicon
 ---
+
+# 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>/
+├── 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.