# PyPotteryLens: An Open-Source Deep Learning Framework for Automated Digitisation of Archaeological Pottery Documentation

arXiv: https://arxiv.org/abs/2412.11574 · HTML: https://mlsanity.com/paper/2412.11574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 66 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Cross-Entropy Loss Functions: Theoretical Analysis and Applications](https://mlsanity.com/paper/2304.07288/) (2023)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [Self-Training: A Survey](https://mlsanity.com/paper/2202.12040/) (2022)
- [DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception](https://mlsanity.com/paper/2410.12628/) (2024)
- [YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions](https://mlsanity.com/paper/2411.00201/) (2024)
- [From paintbrush to pixel: A review of deep neural networks in AI-generated art](https://mlsanity.com/paper/2302.10913/) (2023)
- [AutArch: An AI-assisted workflow for object detection and automated recording in archaeological catalogues](https://mlsanity.com/paper/2311.17978/) (2023)

## Built on by (most cited first, 3 of 3)

- [Rejoining fragmented ancient bamboo slips with physics-driven deep learning](https://mlsanity.com/paper/2505.08601/) (2025)
- [OCR-Based Field Extraction for Archaeological Pottery Metadata: The CENTURIA Dataset](https://mlsanity.com/paper/2608.30616/) (2026)
- [PyPottery: an AI-powered end-to-end suite for pottery processing and publication](https://mlsanity.com/paper/2610.02072/) (2026)

## Abstract (arXiv)

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).