This repository showcases an innovative AI-based project focused on categorizing Dilbert comics into distinct categories and subcategories. By integrating Neural Networks and Transformer models, this project aims to dissect and classify the complex narratives and themes within Dilbert comics, setting a new standard in comics analysis.
- Categorize Dilbert Comics: Systematically classifying Dilbert comics into relevant categories and subcategories.
- Refine Comics Analysis Techniques: Utilizing advanced AI to deepen the understanding of comic strip structures and themes.
- Dataset Creation with Manual Labeling: Building a comprehensive dataset of Dilbert panels and texts, manually labeled with corresponding categories and subcategories.
- Data Structuring and Management: Efficiently organizing and maintaining the Dilbert comics dataset for analysis.
- Neural Networks for Classification: Utilizing CNNs and RNNs to identify and categorize thematic elements in Dilbert comics.
- Natural Language Processing (NLP): Advanced NLP techniques for analyzing and categorizing the textual content of Dilbert strips.
- Transformer Models for Contextualization: Leveraging Transformer models to understand and categorize comics based on deeper semantic relationships.
- Automated Categorization Pipeline: Developing a streamlined process for the automatic categorization of comics into predefined categories and subcategories.
This AI-based approach to categorizing Dilbert comics not only enhances our understanding of this popular comic strip but also sets a precedent for the use of AI in the analysis and classification of sequential art. It demonstrates the potential of AI in providing new insights into the thematic and narrative structures of comics.
Artificial Intelligence Dilbert Comics Comics Categorization Neural Networks Transformer Models NLP Computer Vision Deep Learning Machine Learning Sequential Art Categorization