Documentation
Learn how SuperCatch works, from our advanced card recognition system to marketplace features.
Card Recognition System
SuperCatch uses a sophisticated 3-step recognition pipeline to identify trading cards with maximum accuracy while minimizing costs.
Database Auto-Matching
Our system first attempts to identify front/back orientation and match against known card images:
Front/back detection from filename patterns ("-front", "-back", "-rear")
Image hash comparison against known card images (coming soon)
Visual feature matching using computer vision (coming soon)
✓ If confidence is above 85%, the process stops here with instant results.
OCR Text Extraction
If database matching fails, we use Optical Character Recognition (OCR) to extract text:
Extract player names, years, set names, and card numbers
Identify special features (rookie cards, autographs, serial numbers)
Detect grading information (PSA, BGS, CGC, SGC grades)
Match extracted text against our card database
✓ If confidence is above 75%, the process stops here with structured card data.
AI Recognition (Last Resort)
Only when database and OCR methods fail do we use advanced AI recognition:
GPT-4o-mini vision model analyzes the entire card image
Extracts comprehensive card details including subtle features
Enhanced with database matching for improved accuracy
Cost monitoring ensures efficient usage
✓ This method provides the highest accuracy but is used sparingly to control costs.
Front & Back Card Detection
SuperCatch intelligently groups front and back images of the same card:
Filename analysis: looks for "-front", "-back", or "-rear" indicators
Automatic pairing: first image = front, second image = back (if no filename indicators)
Smart grouping: matches cards with similar attributes but different sides
Visual indicators: shows "Front & Back Detected" with thumbnail previews
Recognition Method Indicators
Each recognized card shows which method was used:
Database auto-matching
OCR text extraction
AI vision recognition
Requires manual entry
Why This Approach Works
Cost Efficient
By trying free and low-cost methods first, we minimize expensive AI usage while maintaining high accuracy.
Fast Results
Database matching provides instant results for known cards, while OCR handles most remaining cases quickly.
High Accuracy
The 3-step pipeline ensures maximum accuracy by using the most appropriate method for each card type.
Tips for Best Results
📁 Use descriptive filenames
Name your files like "2023-topps-chrome-mike-trout-1-front.jpg" for instant database matching
💡 Clear, well-lit photos
Ensure text is readable for optimal OCR performance
🔄 Include both sides
Upload front and back images for complete card information
📷 Supported formats
Use JPEG, PNG, or WebP images up to 10MB each