Genre Classification

Storytelling is a fundamental pillar of childhood development, where visual narratives play a crucial role in enhancing engagement and cognitive processing. While Generative Artificial Intelligence (GAI) has revolutionized content creation, its application for automated story generation from book covers remains largely unexplored. This study presents an innovative pipeline that combines computer vision for genre classification with G AI to create tailored illustrated stories. After evaluating four deep learning architectures widely used in image classification tasks, ConvNeXt-Tiny was selected as the final model, achieving a Weighted F1-score of 0.6898 in categorizing children's books into 13 distinct genres through cover image analysis. To address the lack of benchmark datasets, we compiled and rigorously validated a specialized collection of 4,085 Persian children's book covers. The proposed system leverages both cover design elements and predicted genre features within structured prompts to generate coherent illustrated stories through LLMs and image-synthesis models. A sample of 26 generated stories was qualitatively evaluated by three child psychologists based on narrative coherence, genre alignment, age appropriateness, character continuity, and visual congruence. This research makes significant contributions to both Persian literary analysis and AI-driven creative systems, demonstrating how machine learning can enhance educational storytelling while preserving cultural authenticity.