# PyPottery: an AI-powered end-to-end suite for pottery processing and publication

arXiv: https://arxiv.org/abs/2610.02072 · HTML: https://mlsanity.com/paper/2610.02072/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 1 Oct 2026.

## Builds on (most cited first, 8 of 8)

- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models](https://mlsanity.com/paper/2502.18443/) (2025)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model](https://mlsanity.com/paper/2412.11519/) (2024)
- [PyPotteryLens: An Open-Source Deep Learning Framework for Automated Digitisation of Archaeological Pottery Documentation](https://mlsanity.com/paper/2412.11574/) (2024)
- [PyPotteryInk: One-Step Diffusion Model for Sketch to Publication-ready Archaeological Drawings](https://mlsanity.com/paper/2502.06897/) (2025)
- [Machine learning applications in archaeological practices: a review](https://mlsanity.com/paper/2501.03840/) (2025)
- [An archaeological Catalog Collection Method Based on Large Vision-Language Models](https://mlsanity.com/paper/2412.20088/) (2024)

## Abstract (arXiv)

The study of ceramic materials constitutes a cornerstone of archaeological research, yet the post-production workflow for pottery documentation remains labor-intensive and creates significant publication bottlenecks. This paper presents PyPottery, an open-source, AI-powered suite designed to semi-automate the complete ceramic documentation pipeline. The suite comprises four integrated modules: PyPotteryScan for automated image extraction and handwriting recognition; PyPotteryInk for automatic inking of pencil drawings; PyPotteryTrace for semantically-aware vectorization; and PyPotteryLayout for automated layout generation. Evaluated on 50 hand-drawn sheets containing 240 pottery drawings from the Terramara di Montale (Italy), the framework achieved substantial time savings confirmed by usability study participants, who reported a median perceived speedup of 40× over traditional workflows (range: 17.5×--120×). These results highlight the potential of AI-assisted tools in archaeological documentation, while the paper addresses the strategic redistribution of cognitive labor toward augmentation rather than automation.