This paper presents an integrated deep learning framework that unifies fine-grained corner, edge, and surface evaluation into a coherent grading pipeline, enabling consistent, scalable, and explainable trading card grading for real-world deployment. The proposed system decomposes each card into multiple high-resolution regions—eight corners (front and back), edge patches, and surface segments—each evaluated by specialized deep learning models optimized for distinct defect types. Model outputs are systematically aggregated to produce an overall grade, ensuring both granularity and consistency in evaluation. The modular design supports real-time inference and seamless deployment via FastAPI, enabling scalable industrial adoption. We detail the architecture, deployment pipeline, and performance evaluation, and share key lessons from production integration. Tested on a large industrial dataset, the system achieves high accuracy and exhibits strong agreement with expert human graders. By delivering explainable, consistent, and costefficient grading, this work provides a practical, production-ready alternative to traditional manual grading, addressing a critical bottleneck in the collectible card industry.

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.