The recent developments in the research area of ancient and specialized language processing and recognition are significantly advancing the field. There is a notable trend towards leveraging advanced machine learning techniques, particularly deep learning and large language models (LLMs), to address complex linguistic challenges in domain-specific and historical texts. Innovations in spelling correction, particularly for languages with unique challenges like homophones in Khmer and pinyin abbreviations in Chinese, are being addressed through tailored models that integrate domain-specific knowledge. Additionally, there is a growing focus on the digitization and preservation of ancient scripts, such as Ge'ez and oracle characters, through sophisticated recognition systems that utilize convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. These advancements not only enhance the accuracy and efficiency of text processing but also open new avenues for historical and cultural research. Furthermore, the integration of these technologies into educational tools, such as for dysgraphia detection and speech correction in children, underscores a broader impact on accessibility and learning. Overall, the field is moving towards more specialized, context-aware, and culturally sensitive solutions that promise to revolutionize how we interact with and understand ancient and complex languages.