Findings on Machine Learning for Identification of Archaeological Ceramics: A Systematic Literature Review

The identification of archaeological ceramics is a relevant topic in the field of cultural heritage, and the history of archaeological ceramics can be traced back to prehistoric times. At present, there are two main methods for identifying archaeological ceramics, the empirical method and the technical one. In practice, these methods are costly or time-consuming. A systematic literature review of thirty-three studies on the identification of archaeological ceramics using machine learning is presented in this paper, including the collection process to build the dataset, the image processing of archaeological ceramic images, and the machine learning algorithms used for the classification. The main findings show the efficacy of deep learning for the automatic classification of archaeological ceramics compared to other approaches and highlight the need for more comprehensive and standardised datasets to further improve the automatic classification process.