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README.md

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 license: apache-2.0
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+
+# Introduction
+
+This repository hosts [PaddleOCR PP-OCRv6](https://github.com/PaddlePaddle/PaddleOCR) (the
+[detector](https://huggingface.co/PaddlePaddle/PP-OCRv6_small_det_safetensors) +
+[recognizer](https://huggingface.co/PaddlePaddle/PP-OCRv6_small_rec_safetensors)) for the
+[React Native ExecuTorch](https://www.npmjs.com/package/react-native-executorch) library,
+exported to `.pte` for the **ExecuTorch** runtime (XNNPACK, CoreML and Vulkan backends).
+
+If you'd like to run these models in your own ExecuTorch runtime, refer to the
+[official documentation](https://pytorch.org/executorch/stable/index.html) for setup instructions.
+
+PP-OCRv6 is the **primary** OCR pipeline — smallest and fastest. It ships as **one fused `.pte`** per backend, using **static bucketed
+methods**. The `.pte` is a pure tensor→tensor function; all pre/post-processing (resize,
+normalize, DBNet box decode, perspective crop, CTC decode) is the client's job and is driven by
+`config.json`. One model covers **all languages** (18 709-entry multilingual charset).
+
+## Backends
+
+| backend | target | detect | recognize |
+|---|---|---|---|
+| `xnnpack` | CPU | int8 (DBNet) | fp32 (SVTR) |
+| `coreml` | Apple ANE | weight-only int8 | weight-only int8 |
+| `vulkan` | Android GPU | fp16 (GPU) | fp32 on **XNNPACK** (mixed-delegate) |
+
+> **Vulkan is mixed-delegate**: DBNet detects on the GPU, but the SVTR recognizer runs on the
+> CPU (XNNPACK). int8 SVTR is lossy on the 18 709-token vocab — so the recognizer stays fp32 on CPU for correctness.
+
+## Buckets
+
+`is_bucketed()` reports `[detect sides ; recognize widths]`:
+
+- **detect** (square sides ÷32): `640, 960, 1280` → `detect_640 / detect_960 / detect_1280`
+  (+ a `1280×640` portrait method `detect_640x1280`)
+- **recognize** (widths ÷8, height 48): `160, 320, 480, 640` → `recognize_160 … recognize_640`
+
+Detector input is RGB, normalized `(x/255 − 0.5)/0.5`.
+
+## Compatibility
+
+If you intend to use these models outside of React Native ExecuTorch, make sure your runtime is
+compatible with the **ExecuTorch** version used to export the `.pte` files. For more details, see
+the compatibility note in the
+[ExecuTorch GitHub repository](https://github.com/pytorch/executorch/blob/main/runtime/COMPATIBILITY.md).
+If you work with React Native ExecuTorch, the library constants guarantee compatibility with the
+runtime used behind the scenes.