Beyond Text: Harnessing NLP to Classify Questions Across Modalities

Our research concentrates on building an Natural Language Processing (NLP) model for automatic question classification with the focus on being robust and highly accurate in classification. Auto Categorization is an advanced system capable of automatically organizing questions into given categories using advanced Natural Language Processing (NLP) and Machine Learning techniques. By assigning the most appropriate category to each question, the system enhances information organization and retrieval efficiency. Key features include support for multiple question types, real-time categorization, and continuous learning from user interactions, making it a scalable solution for managing large volumes of data. The system reduces overhead and increases efficiency on retrieval, as well as automates content categorization. This research demonstrates, through Natural Language Processing (NLP) and Machine Learning (ML), how easy it is to manage questions and serve as an intelligent and flexible solution for organizing data. In order to identify both text and image-based inquiries, this study proposes an end-to-end system that integrates Optical Character Recognition (OCR) with a pre-trained language model. Our system allows accurate, real-time classification in educational contexts and accommodates multimodal data, unlike previous approaches that exclusively focus on textual inputs.