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CardSight
Identify Your Cards
Upload a photo of any baseball, football, basketball, hockey, MMA, soccer, Pokémon, Magic: The Gathering, One Piece, Marvel, or Disney card — even a mix in one image — and get instant AI-powered identification
What This Does
- Analyzes card images using AI computer vision
- Detects and identifies multiple cards in a single image
- Automatically detects each card's segment — one image can mix segments (e.g. baseball + Pokémon)
- Returns player name, card number, year, set, release, and manufacturer
- Returns confidence levels (High, Medium, Low) for each detection
- Ranks possible parallel variants (Refractors, Prizms, numbered parallels) with a confidence tier on each candidate — beta, live for baseball
- Suggests alternative card matches on Medium and Low confidence detections so you can pick the right one
- Detects graded slabs and extracts grading company, grade, condition, qualifier, and cert number (PSA, BGS, CGC, SGC, TAG)
- Reads more of the slab label where printed: subgrades, SGC and TAG second scores, Beckett label tier and sub-brand, CGC error designation, and the label's card lines
- Slab-only mode reads graded slab labels without identifying the card, for faster grade and cert lookups
- Reports how many cards were found in the photo and how many of them were identified
- Ranks cards front to back so you can tell which one was held up closest to the camera
- Supports baseball, football, basketball, hockey, MMA, soccer, Pokémon, Magic: The Gathering, One Piece, Marvel, and Disney with more segments coming soon
- Pass a specific segment when you know it (Baseball, Pokémon, One Piece, etc.) — strongly recommended for enhanced accuracy over mixed auto-detect
When To Use This
- You found a card and want to know what it is
- Cataloging your collection quickly
- Scanning multiple cards at once
- Verifying card details for sale/trade
- Reading grades and cert numbers off a stack of graded slabs
- Picking out the card someone is holding up when other cards are in the background
- Building a card scanning mobile app
Real Example
Scenario
You upload a photo of a 1989 Ken Griffey Jr. rookie card
Sample Request
POST
/v1/identify/card(Binary image data)
What You Get Back
{
"success": true,
"requestId": "98cc3c7d-09a1-4433-aa9a-1939a419d411",
"detections": [
{
"confidence": "High",
"card": {
"id": "550e8400-e29b-41d4-a716-446655440000",
"segmentId": "660e8400-e29b-41d4-a716-446655440001",
"releaseId": "770e8400-e29b-41d4-a716-446655440002",
"setId": "880e8400-e29b-41d4-a716-446655440003",
"name": "Ken Griffey Jr.",
"number": "1",
"year": "1989",
"manufacturer": "Upper Deck",
"releaseName": "Upper Deck Baseball",
"setName": "Base Set"
},
"grading": {
"confidence": "High",
"company": {
"id": "11bfc982-39bc-4813-99fc-70483a4dd653",
"name": "PSA"
},
"grade": {
"value": "10",
"condition": "GEM MINT"
},
"certNumber": "87654321",
"labelText": [
"1989 UPPER DECK",
"#1 KEN GRIFFEY JR."
]
},
"zIndex": 0
}
],
"detectedCount": 1,
"identifiedCount": 1,
"processingTime": 1215
}