PyPotteryLens: An Open-Source Deep Learning Framework for Automated Digitisation of Archaeological Pottery Documentation
This work addresses the time-consuming task of digitising legacy pottery data for archaeologists, making advanced digital methods more accessible, though it is incremental as it applies existing computer vision models to a new domain.
The paper tackles the problem of automating the digitisation of archaeological pottery documentation from published sources using deep learning, achieving over 97% precision and recall and reducing processing time by 5x to 20x compared to manual methods.
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 5x to 20x compared to manual methods. Testing across diverse archaeological contexts demonstrates robust generalisation capabilities. 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. The software, documentation, and examples are available on GitHub (https://github.com/lrncrd/PyPottery/tree/PyPotteryLens).