UMass ECE Senior Design Project 2022, Team 24
| Item | Description |
| Card Throughput | >2 cards/min, >1000 card capacity |
| Condition grading | Catch damage visible to the human eye. Provide a valid grading based on that damage based on client guidelines. |
| Card ID | Solution must find the name of the card as well as the set it comes from. >95% accuracy |
| Forgery Detection | Perform weight check, glossiness check, printing verification check. 90% of forgeries fail at least one. |
| Card Damaging | No noticeable new damage to cards after processing |
| Output Bins | At least 15 software-configurable output bins |
The secondhand market for Magic cards is very large, but dealing with cards in bulk requires significant time, manual effort, and money to sort and check for damage. Current automatic sorting solutions, which are themselves quite limited, can’t check for damage properly. Our solution is able to take a large stack of cards as an input, detect and grade each card’s condition, identify the cards, check for forgeries, and sort them into several configurable bins. This allows users to properly value and sort large quantities of cards.
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