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>
31 lines
787 B
Dart
31 lines
787 B
Dart
import 'dart:io';
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import 'package:flutter/services.dart';
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import 'ocr_backend.dart';
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/// Platform OCR backend. Uses the Windows built-in OCR engine exposed through
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/// the native `badnote/ocr` MethodChannel.
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class NativeOcrBackend implements OcrBackend {
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static const _channel = MethodChannel('badnote/ocr');
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@override
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String get name => 'native';
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@override
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Future<bool> isAvailable() async => Platform.isWindows;
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@override
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Future<String?> recognize(Uint8List pngBytes) async {
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if (!Platform.isWindows) return null;
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try {
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final result = await _channel.invokeMethod<String>('recognize', pngBytes);
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final text = result?.trim();
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if (text == null || text.isEmpty) return null;
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return text;
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} catch (_) {
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return null;
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}
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}
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}
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