International Journal of Science, Engineering and TechnologyAuthors: Prof. S. P. Gunjal, Kunal Jagtap, Hrishikesh Joshi, Bhavya Bhat, Anushka Gaikwad
Abstract: The rapid growth of digital documents across domains such as research, business, and education has created significant challenges in efficient information extraction. Traditional methods relying on manual reading or keyword-based search lack contextual understanding and are time-consuming. This paper presents PaperSense, an AI-powered document analyzer that enables users to upload documents and interact with them using natural language queries. The system integrates the Google Gemini 2.5 Flash model for contextual understanding, summarization, and question answering. Built using Python and Streamlit, the system follows a modular architecture separating frontend and backend processing. Experimental evaluation demonstrates improved efficiency in document comprehension and faster information retrieval. The system provides a scalable and user-friendly solution for intelligent document analysis.
