65 lines
2.6 KiB
Markdown
65 lines
2.6 KiB
Markdown
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# AIFotoONLUS Number Recognition Library
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This library provides a small, focused engine to detect and recognize numeric
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text (digits) in images using Darknet (YOLO) models via OpenCvSharp's DNN API.
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It is suitable for batch processing folders of images or individual files.
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Features
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- Detection network (Darknet/Yolo) to find candidate text regions.
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- Recognition network (Darknet/Yolo) to identify digits inside detected crops.
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- Single-file and directory-level processing APIs.
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- Parallel processing with per-thread network instances for throughput.
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- Diagnostic helpers to dump network output shapes and optionally save crop images.
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Basic usage
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1. Create a `ModelConfiguration` instance that points to your Darknet `.cfg`
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and `.weights` files for both detection and recognition networks, configure
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confidence and NMS thresholds and provide a list of number class labels.
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2. Create an instance of `NumberRecognitionEngine`:
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```csharp
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using var engine = new NumberRecognitionEngine(modelConfig, logger: null);
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```
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3. Process a single image:
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```csharp
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var result = engine.ProcessImage("/path/to/image.jpg");
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Console.WriteLine(result.Text);
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```
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4. Process a directory (parallelized):
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```csharp
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var results = await engine.ProcessDirectoryAsync("/path/to/images", recursive: false);
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foreach (var r in results) Console.WriteLine($"{r.FileName}: {r.Text}");
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```
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Configuration notes
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- `ModelConfiguration` controls model file paths, input sizes, thresholds and
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whether to save cropped images for diagnostics. Make sure the paths are
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accessible to the process and the model files match the expected network
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architectures.
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- The engine expects detection network outputs in the YOLO-style layout:
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`[cx, cy, w, h, objectness, class1, class2, ...]`.
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Threading & diagnostics
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- For directory/batch processing the engine creates per-thread Net instances
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so OpenCV forward calls can run concurrently. It also contains fallback
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logic that will perform processing with shared nets under a lock if needed.
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- When `EnableCropSaving` is enabled in configuration, each recognized crop is
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saved to `logs/crops` with a timestamp and optional context label to aid
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debugging false positives/negatives.
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Troubleshooting
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- If the engine returns no detections, verify the model files are correct and
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compatible with the expected output layout. Use
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`ProcessFileWithDiagnostics` to inspect output layer shapes.
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License & Notes
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This project is provided as-is. See repository for licensing information and
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for the model files distribution terms (models are usually not redistributed
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with code and must be obtained separately).
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