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SuperCatch uses a 3-step recognition pipeline to identify cards and limit unnecessary manual review.
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Cheaper and faster matching paths run before the vision model is used.
The pipeline stops as soon as confidence is strong enough for review.
AI Recognition stays targeted instead of running on every upload by default.
When you upload a card image, our system processes it through multiple stages, stopping as soon as it achieves high confidence.
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.
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.
If database and OCR steps do not produce a strong match, we use AI Recognition:
SuperAI vision 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.
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
Each recognized card shows which method was used:
Database auto-matching
OCR Text Extraction
AI vision recognition
Requires manual entry
Free and low-cost steps run first so AI usage stays targeted and review stays efficient.
Database matching resolves known cards first, while OCR handles many of the remaining cases before AI Recognition is needed.
The 3-step pipeline uses the most appropriate recognition step for the card data available.
Name your files like "2023-topps-chrome-mike-trout-1-front.jpg" for instant database matching
Ensure text is readable for optimal OCR performance
Upload front and back images for complete card information
Use JPEG, PNG, or WebP images up to 10MB each
Review Detected Data
Review every detected field before publishing: you can edit card data, condition details, and any recognition result manually before the listing goes live.
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