PyPottery: an AI-powered end-to-end suite for pottery processing and publication
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 37 references that are in the map. First posted on arXiv 1 Oct 2026.
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8 of 37 references.
References · 8
- 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,338
- 2025olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Modelscited by 120
- 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
- 2024LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Modelcited by 5
- 2024PyPotteryLens: An Open-Source Deep Learning Framework for Automated Digitisation of Archaeological Pottery Documentationcited by 3
- 2025PyPotteryInk: One-Step Diffusion Model for Sketch to Publication-ready Archaeological Drawingscited by 3
- 2025Machine learning applications in archaeological practices: a reviewcited by 2
- 2024An archaeological Catalog Collection Method Based on Large Vision-Language Modelscited by 1
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