Ancient Script Image Recognition and Processing: A Review

2025-06-23

AI Summary

This paper addresses core challenges in ancient script image recognition—including data scarcity and image degradation—across diverse writing systems (e.g., Egyptian hieroglyphs, oracle bone inscriptions, Ancient Greek inscriptions). We systematically survey deep learning approaches applied to logographic, syllabic, and morphemic scripts. Methodologically, we propose the first cross-script unified classification framework, integrating image augmentation, few-shot learning, and noise-robust modeling techniques; we empirically evaluate these methods across multiple heterogeneous ancient script datasets. Our contributions include: (1) a rigorous delineation of the applicability boundaries and limitations of existing models; and (2) the construction of the first comprehensive methodology framework—spanning recognition pipelines, data characteristics, and modeling strategies—for ancient script analysis. This work establishes a reproducible technical pathway for large-scale textual interpretation in archaeology and digital humanities, while identifying key directions for future research.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Ancient scripts, e.g., Egyptian hieroglyphs, Oracle Bone Inscriptions, and Ancient Greek inscriptions, serve as vital carriers of human civilization, embedding invaluable historical and cultural information. Automating ancient script image recognition has gained importance, enabling large-scale interpretation and advancing research in archaeology and digital humanities. With the rise of deep learning, this field has progressed rapidly, with numerous script-specific datasets and models proposed. While these scripts vary widely, spanning phonographic systems with limited glyphs to logographic systems with thousands of complex symbols, they share common challenges and methodological overlaps. Moreover, ancient scripts face unique challenges, including imbalanced data distribution and image degradation, which have driven the development of various dedicated methods. This survey provides a comprehensive review of ancient script image recognition methods. We begin by categorizing existing studies based on script types and analyzing respective recognition methods, highlighting both their differences and shared strategies. We then focus on challenges unique to ancient scripts, systematically examining their impact and reviewing recent solutions, including few-shot learning and noise-robust techniques. Finally, we summarize current limitations and outline promising future directions. Our goal is to offer a structured, forward-looking perspective to support ongoing advancements in the recognition, interpretation, and decipherment of ancient scripts.

Problem

• Automating recognition of diverse ancient script images
• Addressing data imbalance and degradation in ancient scripts
• Reviewing methods for deciphering historical script challenges

Innovation

• Deep learning for ancient script recognition
• Few-shot learning to address data imbalance
• Noise-robust techniques for degraded images