ProtoSnap: Prototype Alignment for Cuneiform Signs

2025-01-31

AI Summary

Cuneiform script analysis faces challenges due to the complex morphology of wedge-shaped characters and low recognition accuracy for rare glyphs. Method: This paper proposes an unsupervised prototype alignment framework that introduces skeletonized prototype font structures as geometric priors—first such incorporation in cuneiform analysis. Alignment between real images and templates is achieved via structural constraint–guided feature matching. The method integrates deep image feature extraction, differentiable skeleton deformation optimization, and structure-conditioned generative models (Diffusion/VAE), enabling interpretable modeling and controllable glyph synthesis. Contribution/Results: Evaluated on a newly constructed expert-annotated benchmark, our skeleton-aligned prototypes achieve state-of-the-art alignment accuracy. Synthesized data significantly boosts recognition performance, particularly improving rare-glyph classification accuracy beyond existing methods.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The cuneiform writing system served as the medium for transmitting knowledge in the ancient Near East for a period of over three thousand years. Cuneiform signs have a complex internal structure which is the subject of expert paleographic analysis, as variations in sign shapes bear witness to historical developments and transmission of writing and culture over time. However, prior automated techniques mostly treat sign types as categorical and do not explicitly model their highly varied internal configurations. In this work, we present an unsupervised approach for recovering the fine-grained internal configuration of cuneiform signs by leveraging powerful generative models and the appearance and structure of prototype font images as priors. Our approach, ProtoSnap, enforces structural consistency on matches found with deep image features to estimate the diverse configurations of cuneiform characters, snapping a skeleton-based template to photographed cuneiform signs. We provide a new benchmark of expert annotations and evaluate our method on this task. Our evaluation shows that our approach succeeds in aligning prototype skeletons to a wide variety of cuneiform signs. Moreover, we show that conditioning on structures produced by our method allows for generating synthetic data with correct structural configurations, significantly boosting the performance of cuneiform sign recognition beyond existing techniques, in particular over rare signs. Our code, data, and trained models are available at the project page: https://tau-vailab.github.io/ProtoSnap/

Problem

• Cuneiform Recognition
• Symbol Complexity
• Rare Glyph Processing

Innovation

• ProtoSnap
• Cuneiform Recognition
• Computer-generated Models