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>
20 lines
763 B
Dart
20 lines
763 B
Dart
import 'dart:typed_data';
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/// A pluggable local OCR backend.
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///
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/// Implementations turn a PNG image into recognized text. The app selects an
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/// available backend via [OcrBackends]; absence of any backend is a clean
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/// no-op (recognition returns null).
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abstract class OcrBackend {
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/// Short identifier used for logging/selection (e.g. 'native', 'onnx').
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String get name;
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/// Whether this backend can run on the current device/build. May perform a
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/// lazy initialization attempt (e.g. loading a model) the first time.
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Future<bool> isAvailable();
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/// Recognize text from a PNG image. Returns null when nothing is recognized
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/// or the backend is unavailable. Implementations must never throw.
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Future<String?> recognize(Uint8List pngBytes);
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}
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