Edge Grading in Trading Cards Using Transfer Learning: Methods, Experiments, and Evaluation

The trading card market is a dynamic industry where card values depend on meticulous grading of condition, authenticity, and quality. Traditionally, grading is performed manually by expert assessors, but this process is prone to subjectivity and inconsistency. Automated grading systems could ensure greater objectivity and uniformity in assessments. Key grading factors include centering, edges, corners, and surface quality. While our previous work focused on corner grading, this paper explores edge grading using CNN and transfer learning models such as DenseNet, ResNet, and VGG. By fine-tuning, ResNet50 achieved 93% accuracy on a dataset from our industry partner. To address uncertainty in grading, various calibration methods are employed, and a human-in-the-loop approach enhances robustness. A final scoring method provides an objective edge grading for each card.