# Image Preprocessing for Historical Document OCR

This document outlines the enhanced preprocessing capabilities for improving OCR quality on historical documents, including deskewing, thresholding, and morphological operations.

## Overview

The preprocessing pipeline offers several options to enhance image quality before OCR processing:

1. **Deskewing**: Automatically detects and corrects document skew using multiple detection algorithms
2. **Thresholding**: Converts grayscale images to binary using adaptive or Otsu methods with pre-blur options
3. **Morphological Operations**: Cleans up binary images by removing noise or filling in gaps
4. **Document-Type Specific Settings**: Customized preprocessing configurations for different document types

## Configuration

Preprocessing options are set in `config.py` and are tunable per document type. All settings are accessible through environment variables for easy deployment configuration.

### Deskewing

```python
"deskew": {
    "enabled": True/False,              # Whether to apply deskewing
    "angle_threshold": 0.1,             # Minimum angle (degrees) to trigger deskewing
    "max_angle": 45.0,                  # Maximum correction angle
    "use_hough": True/False,            # Use Hough transform in addition to minAreaRect
    "consensus_method": "average",      # How to combine angle estimations
    "fallback": {"enabled": True/False} # Fall back to original if deskewing fails
}
```

Deskewing uses two methods:
- **minAreaRect**: Finds contours in the binary image and calculates their orientation
- **Hough Transform**: Detects lines in the image and their angles

The `consensus_method` can be:
- `"average"`: Average of all detected angles (most stable)
- `"median"`: Median of all angles (robust to outliers)
- `"min"`: Minimum absolute angle (most conservative)
- `"max"`: Maximum absolute angle (most aggressive)

### Thresholding

```python
"thresholding": {
    "method": "adaptive",               # "none", "otsu", or "adaptive"
    "adaptive_block_size": 11,          # Block size for adaptive thresholding (must be odd)
    "adaptive_constant": 2,             # Constant subtracted from mean
    "otsu_gaussian_blur": 1,            # Blur kernel size for Otsu pre-processing
    "preblur": {
        "enabled": True/False,          # Whether to apply pre-blur
        "method": "gaussian",           # "gaussian" or "median"
        "kernel_size": 3                # Blur kernel size (must be odd)
    },
    "fallback": {"enabled": True/False} # Fall back to grayscale if thresholding fails
}
```

Thresholding methods:
- **Otsu**: Automatically determines optimal global threshold (best for high-contrast documents)
- **Adaptive**: Calculates thresholds for different regions (better for uneven lighting, historical documents)

### Morphological Operations

```python
"morphology": {
    "enabled": True/False,              # Whether to apply morphological operations
    "operation": "close",               # "open", "close", "both"
    "kernel_size": 1,                   # Size of the structuring element
    "kernel_shape": "rect"              # "rect", "ellipse", "cross"
}
```

Morphological operations:
- **Open**: Erosion followed by dilation - removes small noise and disconnects thin connections
- **Close**: Dilation followed by erosion - fills small holes and connects broken elements
- **Both**: Applies opening followed by closing

### Document Type Configurations

The system includes optimized settings for different document types:

```python
"document_types": {
    "standard": {
        # Default settings - will use the global settings
    },
    "newspaper": {
        "deskew": {"enabled": True, "angle_threshold": 0.3, "max_angle": 10.0},
        "thresholding": {
            "method": "adaptive", 
            "adaptive_block_size": 15,
            "adaptive_constant": 3,
            "preblur": {"method": "gaussian", "kernel_size": 3}
        },
        "morphology": {"operation": "close", "kernel_size": 1}
    },
    "handwritten": {
        "deskew": {"enabled": True, "angle_threshold": 0.5, "use_hough": False},
        "thresholding": {
            "method": "adaptive", 
            "adaptive_block_size": 31, 
            "adaptive_constant": 5,
            "preblur": {"method": "median", "kernel_size": 3}
        },
        "morphology": {"operation": "open", "kernel_size": 1}
    },
    "book": {
        "deskew": {"enabled": True},
        "thresholding": {
            "method": "otsu",
            "preblur": {"method": "gaussian", "kernel_size": 5}
        },
        "morphology": {"operation": "both", "kernel_size": 1}
    }
}
```

## Performance and Logging

```python
"performance": {
    "parallel": {
        "enabled": True/False,          # Whether to use parallel processing
        "max_workers": 4                # Maximum number of worker threads
    },
    "timeout_ms": 10000                 # Timeout for preprocessing (in milliseconds)
}

"logging": {
    "enabled": True/False,              # Whether to log preprocessing metrics
    "metrics": ["skew_angle", "binary_nonzero_pct", "processing_time"],
    "output_path": "logs/preprocessing_metrics.json"
}
```

## Usage with OCR Processing

When processing documents, simply specify the document type:

```python
preprocessing_options = {
    "document_type": "newspaper",  # Use newspaper-optimized settings
    "grayscale": True,             # Legacy option: apply grayscale conversion
    "denoise": True,               # Legacy option: apply denoising
    "contrast": 10,                # Legacy option: adjust contrast (0-100)
    "rotation": 0                  # Legacy option: manual rotation (degrees)
}

# Apply preprocessing and OCR
result = process_file(file_bytes, file_ext, preprocessing_options=preprocessing_options)
```

## Visual Examples

### Original Document
*[A historical newspaper or document image would be shown here]*

### After Deskewing
*[The same document, with skew corrected]*

### After Thresholding
*[The document converted to binary with clear text]*

### After Morphological Operations
*[The binary image with small noise removed and/or gaps filled]*

## Troubleshooting

### Poor Deskewing Results
- **Symptom**: Document skew is not correctly detected or corrected
- **Solution**: Try adjusting `angle_threshold` or `max_angle`, or disable Hough transform for handwritten documents

### Thresholding Issues
- **Symptom**: Text is lost or background noise is excessive after thresholding
- **Solution**: Try changing the thresholding method or adjusting `adaptive_block_size` and `adaptive_constant`

### Performance Concerns
- **Symptom**: Processing is too slow for large documents
- **Solution**: Enable parallel processing, reduce image size, or disable some preprocessing steps for faster results