AI card recognition
SuperCatch uses a 3-step recognition pipeline to identify cards and limit unnecessary manual review.
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Recognition pipeline
When you upload a card image, our system processes it through multiple stages, stopping as soon as it achieves high confidence.
Database auto-matching
FreeOur 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.
OCR text extraction
Low CostIf 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)
Higher CostIf database and OCR steps do not produce a strong match, we use AI recognition:
GPT-4o-mini vision model analyzes the entire card image
Extracts card details that were not resolved earlier in the pipeline
Enhanced with database matching for improved accuracy
Cost monitoring keeps AI usage targeted
✓ This step runs only when earlier recognition steps do not produce a strong match.
Front and 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
Free and low-cost steps run first so AI usage stays targeted and review stays efficient.
Fast results
Database matching resolves known cards first, while OCR handles many of the remaining cases before AI recognition is needed.
High accuracy
The 3-step pipeline uses the most appropriate recognition step for the card data available.
Supported card types
Sports Cards
Baseball
Basketball
Football
Hockey
Soccer
UFC/MMA
Trading card games
Pokémon
Magic: The Gathering
Yu-Gi-Oh!
One Piece
Dragon Ball
Other collectibles
Non-Sports Cards
Autographs
Video Games
Tickets & Memorabilia
Recognition tips
Photo and upload tips
📁 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
Note
Note: Review detected information before publishing your listing. You can edit any field manually.