Recognition of Three Fine-Grades of Ancient Roman Coins Through Transfer Learning

Ancient coins are valuable historical artefacts for revealing insights into ancient civilizations and cultural practices. Typically, numismatists grade the coins relying on their own experience rather than on science and objective measurement tools. For ensuring reliability of a coin-grade system it has to be reproducible and this can only be achieved by building a system that does not fully rely on manual object inspection. In this work, we propose a transfer learning approach to grade ancient Roman coins. In particular, we use coin data comprising three fine grades, such as Fine (F), Very-Fine (VF), and Extremely-Fine (EF) coins. The transfer learning implemented yielded a reliable result in recognizing the grade of these ancient Roman coins. By building a robust data processing pipeline and leveraging transfer learning, an accuracy of 0.84 was achieved. This result shows promise for automated coin grading or assisting manual grading processes.