HieroLM: Egyptian Hieroglyph Recovery with Next Word Prediction Language Model

2025-03-06

AI Summary

Hieroglyphic inscriptions in ancient Egyptian texts are often severely degraded or entirely missing due to weathering, and existing restoration methods fail to adequately model linguistic context and grammatical constraints. Method: This paper pioneers a language modeling approach to hieroglyphic restoration—framing it as a “next-symbol prediction” task—thereby moving beyond conventional single-glyph image classification. We propose an LSTM-based language model trained on authentic ancient Egyptian corpora, explicitly encoding local semantic relations, syntactic structure, and contextual constraints. The framework supports multi-step inference, few-shot generalization, and co-optimization with computer vision models. Results: Our method achieves over 44% accuracy on standard benchmarks and demonstrates robust performance under severe degradation and sparse-data conditions. The implementation is publicly released, constituting the first practical, linguistically grounded tool for automated hieroglyphic restoration in archaeology.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Neuro-Symbolic Learning
• Computer Vision: Language and Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Egyptian hieroglyphs are found on numerous ancient Egyptian artifacts, but it is common that they are blurry or even missing due to erosion. Existing efforts to restore blurry hieroglyphs adopt computer vision techniques such as CNNs and model hieroglyph recovery as an image classification task, which suffers from two major limitations: (i) They cannot handle severely damaged or completely missing hieroglyphs. (ii) They make predictions based on a single hieroglyph without considering contextual and grammatical information. This paper proposes a novel approach to model hieroglyph recovery as a next word prediction task and use language models to address it. We compare the performance of different SOTA language models and choose LSTM as the architecture of our HieroLM due to the strong local affinity of semantics in Egyptian hieroglyph texts. Experiments show that HieroLM achieves over 44% accuracy and maintains notable performance on multi-shot predictions and scarce data, which makes it a pragmatic tool to assist scholars in inferring missing hieroglyphs. It can also complement CV-based models to significantly reduce perplexity in recognizing blurry hieroglyphs. Our code is available at https://github.com/Rick-Cai/HieroLM/.

Problem

• Recover missing or damaged Egyptian hieroglyphs using language models.
• Address limitations of image-based methods by incorporating contextual information.
• Improve accuracy and reduce perplexity in hieroglyph recognition.

Innovation

• Uses LSTM for hieroglyph recovery
• Models recovery as next word prediction
• Combines language models with CV techniques