| --- |
| license: apache-2.0 |
| pipeline_tag: image-classification |
| library_name: executorch |
| --- |
| |
| # Introduction |
|
|
| This repository hosts the **PaddleOCR document helper models** — page-orientation |
| classification, dewarping, and table-structure recognition — for the |
| [React Native ExecuTorch](https://www.npmjs.com/package/react-native-executorch) library, |
| fused into **one multi-method `.pte`** per backend for the **ExecuTorch** runtime |
| (XNNPACK, CoreML, Vulkan). These are document pre/post-processing companions to |
| [`react-native-executorch-pp-ocrv6`](https://huggingface.co/software-mansion/react-native-executorch-pp-ocrv6) — |
| not an OCR model on their own. |
|
|
| 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. |
|
|
| ## Repository layout |
|
|
| ``` |
| <backend>/config.json # per-backend spec |
| <backend>/paddle_helpers_<backend>_<precision>.pte |
| ``` |
|
|
| ## Methods |
|
|
| The fused `.pte` exposes **four methods** (the `.pte` is pure tensor→tensor; the client |
| does normalization, `argmax`/`softmax`, grid-sampling and the decode loop). All methods are |
| **fixed-shape** — the exact input shapes below are also declared in `config.json`, and since |
| nothing here is dynamic the model carries no `get_model_schema` overrides; a client reads the |
| signatures straight from ExecuTorch's `MethodMeta`. |
|
|
| | method | source model | input | output | purpose | |
| |---|---|---|---|---| |
| | `orientation` | [PP-LCNet doc_ori](https://huggingface.co/PaddlePaddle/PP-LCNet_x1_0_doc_ori_safetensors) | `[1,3,224,224]` | `logits[1,4]` | page rotation **0 / 90 / 180 / 270°** (argmax) | |
| | `dewarp` | [UVDoc](https://huggingface.co/PaddlePaddle/UVDoc_safetensors) | `[1,3,712,488]` | `grid[1,2,45,31]` | sampling grid → `grid_sample` to unwarp a curved/folded page | |
| | `table_encode` | [SLANet_plus](https://huggingface.co/PaddlePaddle/SLANet_plus_safetensors) | `[1,3,488,488]` | `feat[1,256,96]` | encode a cropped table image (run **once**) | |
| | `table_decode_step` | SLANet_plus decoder | `(feat[1,256,96], hidden[1,256], onehot[1,50])` | `(probs[1,50], hidden[1,256])` | one **autoregressive** structure-token step | |
| |
| The table decode loop is data-dependent, so it stays on the client: run `table_encode` once, |
| then call `table_decode_step` per token, feeding the previous step's hidden state back in and |
| the chosen token back as a one-hot. The first step takes an **all-zero** one-hot — the exported |
| decoder embeds its own start token. |
|
|
| ## Backends, precision & latency (warm) |
|
|
| | backend | target | precision | size | orientation / table_encode / dewarp / decode_step | |
| |---|---|---|---|---| |
| | `xnnpack` | CPU | **dynamic int8** (qd8 — lossless; static int8 is lossy on the regressors) | ~28 MB | 2.0 / 30 / 209 / 0.21 ms (Galaxy S24) | |
| | `coreml` | Apple ANE | weight-only int8 | **11.9 MB** | 0.3 / 1.4 / 4.7 / 0.13 ms (Apple M-series ANE) | |
| | `vulkan` | Android GPU | fp16, except the table path → XNNPACK (mixed-delegate) | 23 MB | 6.0 / 17 / 66 / 0.21 ms (Galaxy S24) | |
|
|
| - The GPU wins on the heavy CNNs (dewarp 3×); the tiny `orientation` and the dispatch-bound |
| autoregressive `table_decode_step` are faster on CPU — hence the Vulkan build routes the |
| table path to XNNPACK. |
| - `table_decode_step` is always computed at full precision on CPU (autoregressive stability). |
|
|
| ## 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. |
|
|
| These models were exported with ExecuTorch 1.3.1 and **no forward compatibility** is |
| guaranteed; older runtimes may not load them. |
|
|
