PyPotteryLens: An open-source deep learning framework for automated digitisation of archaeological pottery documentation

Archaeological pottery documentation and study represents a crucial but time-consuming aspect of archaeology. While recent years have seen advances in digital documentation methods, vast amounts of legacy data remain locked in traditional publications. This paper introduces PyPotteryLens , an open-source framework that leverages deep learning to automate the digitisation and processing of archaeological pottery drawings from published sources. The system combines state-of-the-art computer vision models (YOLO for instance segmentation and EfficientNetV2 for classification) with an intuitive user interface, making advanced digital methods accessible to archaeologists regardless of technical expertise. The framework achieves over 97% precision and recall in pottery detection and classification tasks, while reducing processing time by up to 5× to 20× compared to manual methods. Also, the system’s modular architecture facilitates extension to other archaeological materials, while its standardised output format ensures long-term preservation and reusability of digitised data as well as solid basis for training machine learning algorithms. • Introduces PyPotteryLens, a deep learning framework that automates pottery documentation. • Implements instance segmentation through YOLO architecture for precise pottery drawing extraction. • Develops an intuitive graphical interface that democratises computational methods in archaeology. • Establishes an open-source, modular architecture that facilitates community-driven development. • Enables systematic unsupervised learning analysis of large pottery assemblages.