Using Computer Vision to Make Card Grading Faster and Cheaper

James Nitsch, a mobile developer, explores how computer vision can address the challenges associated with grading collectible cards, a process that is traditionally expensive and time-consuming. By developing a proof-of-concept model using images of Pokémon cards, Nitsch demonstrates that computer vision techniques can assist in evaluating card conditions by detecting features such as edge wear, corner wear, and damage. Leveraging tools like web scrapers built in Python and Roboflow's image annotation and augmentation utilities, the model aims to streamline the grading process, potentially offering amateur collectors a more accessible way to assess card value and authenticity. Despite the model's ongoing development, the goal is to deploy it via an API to allow users to upload card images and receive quality and pricing estimates. This approach could significantly reduce the reliance on slow and costly professional grading services, enabling collectors to better navigate market trends and valuation surges.