Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle bones-has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.

翻译：甲骨文识别——对甲骨上古代中国铭文的分析——已成为考古学、古文字学与历史文化研究交叉的关键领域。传统的甲骨文识别方法主要依赖专家的手工解读，这不仅劳动密集，也限制了公众更广泛的接触。随着模式识别与深度学习领域的最新突破，甲骨文识别（OrCR）的自动化趋势日益显著，在应对这些古老文字固有挑战方面展现出巨大潜力。然而，对OrCR的全面理解仍显不足。为此，本文对当前OrCR研究现状进行了系统化、结构化的综述。我们首先识别并分析了OrCR面临的关键挑战。随后，概述了可用于OrCR的主要基准数据集与数字资源。接着对当代研究方法进行了回顾，重点批判性地评述了各自在应对甲骨文复杂特性方面的效能、局限性与适用性。此外，我们的综述还延伸至跨学科领域中与OrCR相关的辅助任务，对其应用进行了广谱分析。最后，我们以前瞻性视角作结，提出了可能推动该领域取得重大进展的未来研究方向。