oar-ocr 0.8.0

# Frequently Asked Questions

Common build and runtime questions, distilled from GitHub issues.

## Windows: linking fails with `LNK2001: unresolved external symbol __std_find_trivial_8`

Unresolved `__std_*` symbols (`__std_find_trivial_*`, `__std_max_element_*`,
`__std_search_*`, ...) mean the linker's MSVC STL is too old. The prebuilt
ONNX Runtime binaries that `ort-sys` downloads are compiled with Visual
Studio 2022 (MSVC v143) and reference vectorized STL helpers that do not
exist in the VS 2019 (v142) link libraries.

Fix:

1. Install **Visual Studio 2022** or **Build Tools for Visual Studio 2022**
   with the "Desktop development with C++" workload (MSVC v143 + Windows SDK).
2. Run `cargo clean` and rebuild — rustc picks the newest installed MSVC
   toolset automatically.

VS 2019 and VS 2022 build tools can coexist; only the newer one needs to be
present for linking. (See issue [#105](https://github.com/GreatV/oar-ocr/issues/105).)

## GPU inference is slower than CPU for PP-OCRv6 tiny/small

Expected. The tiny/small models are so small that per-call overhead —
host↔device tensor copies, kernel launches, CPU/GPU synchronization, plus
pre/post-processing that always runs on the CPU — outweighs the compute the
GPU saves. Measured on an RTX 4090 + i9-13900KF (single image, warmup
excluded):

| Model  | CPU        | GPU (CUDA EP)             |
| ------ | ---------- | ------------------------- |
| tiny   | 34 ms/img  | 44 ms/img                 |
| small  | 59 ms/img  | 77 ms/img                 |
| medium | 404 ms/img | **173 ms/img** (2.3× faster) |

Guidelines:

- For tiny/small, use the default CPU mode (the `simd` feature is on by default).
- Use the medium model, or batch several images per `predict()` call, when
  GPU acceleration matters.
- Exclude the first call when benchmarking: it includes cuDNN initialization
  and algorithm selection (~5× slower than steady state).

Also note that requesting `OrtExecutionProvider::CUDA` without building with
`--features cuda` makes the pipeline builder return an error — check the
`Result` of `.build()`. Without the `cuda` feature, the downloaded ONNX
Runtime is CPU-only and the GPU is never used.
(See issue [#151](https://github.com/GreatV/oar-ocr/issues/151).)