AI Analysis of Arabic Calligraphy Characteristics in Digital Advertisements

In the realm of digital marketing across Arabic-speaking demographics, Arabic calligraphy plays a crucial role as a fundamental component affecting audience interaction, comprehension, and sentiment interpretation through strings of text. Therefore, by applying machine learning and computer vision techniques, this study investigates how varying calligraphy styles, contrast levels, and complexity influence the effectiveness of digital advertisements. The study analyzes a dataset of Arabic calligraphy-based advertisements using a Support Vector Machine (SVM) model, evaluating them on engagement metrics, readability scores, and sentiment analysis to find the best design approaches. The findings suggest that modern and hybrid calligraphy styles increase engagement, high contrast improves readability, and moderate complexity elicits the most positive emotional responses. These results may provide insights for advertisers and designers looking to improve the effectiveness of their digital campaigns while also protecting and preserving their artistic heritage. This study shows the increasing influence of AI in creative fields and provides a basis for further research into AI-based typography analysis.