Computational Approaches to Ancient Manuscript Digitization and Pedagogical Implementation

Abstract

The current paper addresses the challenges of digitalizing ancient documents, particularly those with multilingual and non-standard scripts, by refining AI-based models and image processing systems. The importance of this study is based on the weaknesses of conventional OCR systems to work with damaged and complex texts. Text recognition was improved with the help of AI-based models, and image processing was improved with the help of advanced image processing. The paper also looks at the ways these digitized manuscripts may be integrated in an interactive and collaborative model of education. Quantitative approach used for this research. The research finds that AI-based models are much more effective than traditional OCR tools and have an average accuracy of 95.4% compared to 81.2. The cool image processing techniques helped a great deal in enhancing the legibility of the texts and minimizing distortion (p-value < 0.01). There was also a high engagement, comprehension, and retention of students with an effect size of 1.6 in the experimental group where digitized materials were applied. These findings indicate the potential of AI and enhanced processing not only to conserve old texts but also to enhance educational success, which offers a good solution to the issue of manuscript digitization and teaching.