To extend the proposed method for a broader range of comic book styles and genres, including non-English comics, several strategies can be implemented:
Multilingual Large Language Models (LLMs): Utilizing multilingual LLMs can facilitate the understanding and generation of text in various languages. By training or fine-tuning these models on diverse datasets that include non-English comics, the system can better handle language-specific nuances, idioms, and cultural references.
Diverse Dataset Collection: Expanding the dataset to include a variety of comic styles, such as manga, webtoons, and graphic novels from different cultures, will enhance the model's ability to recognize and describe unique artistic elements and storytelling techniques. This includes collecting comics from various regions, ensuring representation of different artistic styles and narrative structures.
Style-Specific Prompt Engineering: Developing tailored prompt engineering techniques for different comic styles can improve the accuracy of character identification and dialogue generation. For instance, prompts can be adjusted to account for the unique visual language of manga, which often includes specific panel layouts and character expressions.
Visual Feature Adaptation: Implementing advanced computer vision techniques that are sensitive to the stylistic differences in comic art can enhance character recognition and scene description. This may involve training models on style-specific features, such as line thickness, color palettes, and panel arrangements.
User Feedback Mechanisms: Incorporating user feedback from blind and low-vision readers can help refine the model's outputs. By understanding the preferences and needs of users from different cultural backgrounds, the system can be iteratively improved to provide more relevant and engaging content.