OCR: embedded, cross-platform ONNX backend with pluggable fallback

Make on-device OCR a pluggable local service so it runs locally on every
platform (not just Windows), aimed at GoodNotes/Notability-class handwriting on
low-power hardware (e.g. Zen2 APU, CPU/iGPU).

- New OcrBackend abstraction (lib/services/ocr/): selector prefers an embedded
  ONNX recognition backend, falling back to the OS-native backend (Windows
  WinRT), and to a clean no-op when neither is available.
- OnnxRecognitionBackend: flutter_onnxruntime session from a bundled asset,
  dart:ui preprocessing (resize to 48px, CHW float32, normalized), pure-Dart CTC
  greedy decode. Fully guarded — absent model/dict is a no-op; never throws.
- ocr_engine.dart kept as a thin facade (recognizeImage) delegating to the
  selector, so ocr_service.dart is unchanged.
- CtcDecoder unit-tested (6 tests). flutter analyze clean; all tests pass.
- Model is not committed; tool/fetch_ocr_model.sh + assets/models/ocr/README.md
  document fetching PP-OCRv4 rec + dict on the dev machine.
- CI: forward HTTPS_PROXY to the Windows build so CMake can fetch the ONNX
  Runtime native lib behind the GFW; README documents the system-install
  alternative. PP-OCR geometry/blank assumptions documented for on-device tuning.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

assets/models/ocr/README.md

@@ -0,0 +1,63 @@
+# Embedded OCR model (not committed)
+
+The handwriting/text recognition backend
+(`lib/services/ocr/onnx_recognition_backend.dart`) loads an ONNX recognition
+model and its character dictionary **from assets**:
+
+- `rec.onnx` — the PP-OCRv4 mobile text recognition model (CTC, input
+  `3 x 48 x W`, blank class index 0).
+- `ppocr_keys_v1.txt` — the PP-OCR character dictionary, one character per line.
+
+Neither file is committed to the repository (the model is large and the
+dictionary is distributed with PaddleOCR). The app is built to treat their
+absence as a clean no-op: if the model or dictionary is missing, the ONNX
+backend reports unavailable and OCR falls back to the native platform backend
+(or returns nothing). Only the `.gitkeep` placeholder is committed so the
+`assets/models/ocr/` asset directory is valid at build time.
+
+## How to obtain and place the files
+
+Run the helper script on your development machine (it must download from the
+PaddleOCR sources and convert the Paddle inference model to ONNX):
+
+```bash
+./tool/fetch_ocr_model.sh
+```
+
+This places the two files here as:
+
+```
+assets/models/ocr/rec.onnx
+assets/models/ocr/ppocr_keys_v1.txt
+```
+
+### Sources
+
+- PP-OCRv4 mobile recognition model (PaddleOCR inference model):
+  https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar
+  (English-only variant: `en_PP-OCRv4_rec_infer.tar`)
+- Character dictionary `ppocr_keys_v1.txt`:
+  https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/main/ppocr/utils/ppocr_keys_v1.txt
+
+### Conversion
+
+PaddleOCR ships Paddle inference models; convert to ONNX with
+[paddle2onnx](https://github.com/PaddlePaddle/Paddle2ONNX):
+
+```bash
+paddle2onnx \
+  --model_dir ch_PP-OCRv4_rec_infer \
+  --model_filename inference.pdmodel \
+  --params_filename inference.pdiparams \
+  --save_file rec.onnx \
+  --opset_version 14 \
+  --enable_onnx_checker True
+```
+
+## Verification note
+
+The backend assumes the PP-OCRv4 mobile rec convention (input `3 x 48 x W`,
+normalization `(v/255 - 0.5)/0.5`, CTC blank at index 0, dictionary shifted by
+one). If you use a different exported model, verify the input shape,
+normalization, and blank/dictionary convention and adjust
+`onnx_recognition_backend.dart` / `CtcDecoder` accordingly.