用语言模型预测下一个象形文字，修复破损古埃及文字。
HieroLM: Egyptian Hieroglyph Recovery with Next Word Prediction Language Model
- 将象形文字恢复视为文本续写任务，利用语言模型建模上下文。
- 在多轮预测和数据稀缺场景下仍达44%以上准确率。
- 适合考古学者辅助推断缺失文字，可与视觉模型互补。
古埃及象形文字广泛存在于古代文物上，但常因风化导致模糊甚至缺失。现有修复方法多采用计算机视觉技术（如CNN），将恢复任务视为图像分类，存在两大局限：(i) 无法处理严重损坏或完全缺失的象形文字；(ii) 仅基于单个符号进行预测，忽略上下文与语法信息。本文提出将象形文字恢复建模为下一个词预测任务，使用语言模型解决该问题。我们对比了多种SOTA语言模型，最终选用LSTM作为HieroLM架构，因其对古埃及象形文字语义局部关联性强的适配性。实验表明，HieroLM在多轮预测和小样本条件下均保持超过44%的准确率，具备实际应用价值，可辅助学者推断缺失文字，并显著降低模糊文字识别的困惑度。代码已公开于https://github.com/Rick-Cai/HieroLM/。
原文摘要 · Abstract (English)
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/.
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