Classification of Bangla Book Genres Using Book Cover and Title

Abstract

The rapid expansion of e-books and digital libraries has underscored the need for efficient and accurate genre classification systems, particularly for information retrieval, content filtering, and book recommendation purposes. This research "Classification of Bangla Book Genres using Book Cover and Title," aims to develop a reliable system that leverages both linguistic and visual data for the accurate classification of Bangla book genres. A comprehensive dataset of over 15,000 Bangla book covers and titles spanning various genres was collected through web scraping from the Rokomari e-commerce site and annotated for training and evaluating the models. Transfer learning architectures employing pre-trained models like Xception were implemented for feature extraction from images, while the BanglaBERT model was used to obtain contextualized word embeddings for book titles. The core of the research involves a comparative analysis of four distinct machine learning and deep learning models: Logistic Regression, Random Forest, Support Vector Machine, and Neural Network. The findings reveal that the Neural Network model emerges as the front-runner, achieving the highest overall accuracy of 78%. This superior performance underscores the model's ability to generalize well and capture robust features essential for distinguishing between diverse Bangla book genres. This research demonstrates the transformative potential of automated content classification systems in enhancing the accessibility and global reach of Bangla literature, thereby bridging the gap between traditional literary classification and modern digital behaviors.