# System Patterns

## Model Architecture
1. Core ML Integration
   - Vision framework for image handling
   - Direct category mapping
   - No intermediate transformations

2. Classification Flow
   ```
   Image → VNImageRequestHandler → VNCoreMLRequest → VNClassificationObservation → SquareClassification
   ```

3. Category System
   - 12 piece categories:
     * white_pawn to white_king
     * black_pawn to black_king
   - Empty square fallback
   - No background variations in model

## Processing Pipeline
1. Board Detection
   - VNDetectRectanglesRequest
   - Aspect ratio validation
   - Square extraction

2. Image Processing
   - Contrast enhancement
   - Edge sharpening
   - Noise reduction
   - Center crop

3. Classification
   - Vision framework integration
   - Confidence threshold
   - Error handling

## Code Organization
1. Recognition Layer
   ```
   PieceRecognizer
   ├── Model loading
   ├── Image preprocessing
   └── Classification handling
   ```

2. Model Layer
   ```
   SquareClassification
   ├── Category mapping
   ├── Piece type/color
   └── Empty square handling
   ```

3. Core Components
   ```
   BoardDetector
   ├── Rectangle detection
   ├── Square extraction
   └── Position validation
   ```

## Data Flow
1. Capture
   ```
   ScreenCapture → Raw Image → Board Rectangle
   ```

2. Processing
   ```
   Board Rectangle → Individual Squares → Preprocessed Images
   ```

3. Classification
   ```
   Preprocessed Images → ML Model → Piece Categories → Chess Position
   ```

## Key Patterns
1. Direct Integration
   - Vision framework throughout
   - No intermediate conversions
   - Consistent image handling

2. Error Handling
   - Early validation
   - Graceful fallbacks
   - Detailed logging

3. Performance
   - Shared CIContext
   - Efficient image processing
   - Optimized model loading