Image Processing Techniques In A Python Course Based on Ancient Manuscript Processing

There is a need to develop effective curricula based on real-life cases that can be used to broaden students’ participation and motivate them during their studies. Real-life case examples are crucial to justify the learning of the respective discipline. In our study, we explore the application of image processing using Python in the context of processing ancient manuscripts, particularly palimpsests. Palimpsests are invaluable historical manuscripts, often rewritten and reused over time. They pose challenges for researchers due to legibility and interpretation issues. Python and AI can help decipher them. This interdisciplinary study not only introduces students to applied AI techniques, but also enriches their practice with the use of various image preprocessing techniques to enhance the readability of palimpsests. Specific methods such as Gamma correction, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gaussian smoothing are detailed to demonstrate various preprocessing strategies. Current educational trends in Applied Artificial Intelligence (AAI) are discussed in this paper. Experimental techniques are elucidated through example code implementations showing the practical application of these technologies. The research contributes to the advancement of both educational and scientific applications by introducing learners to the history of their country through the analysis of ancient manuscripts. By engaging with these materials, students gain practical skills in AI while deepening their historical knowledge, fostering a holistic and interdisciplinary approach to learning.