Published Paper Details:

The aim of this research is to automate and enhance the inspection process of educational institutions using Artificial Intelligence. Traditional inspections often suffer from inefficiencies and lack of transparency. To address this, an AI-driven inspection system was developed, integrating image processing, document analysis, and geolocation verification. The system utilizes OpenCV and PyTesseract for detecting facility conditions such as cleanliness, damage, and fire safety compliance from images. Furthermore, document uploads are analyzed using NLP to extract key details like faculty qualifications and institutional compliance. A GPS-based authenticity check ensures the submitted data is genuine and captured on-site. MongoDB is used for structured data storage and retrieval, while Streamlit serves as the user interface for inspection and reporting. The results include real-time inspection summaries, visual analytics, and automated compliance scoring, significantly improving efficiency and accuracy in the inspection process. The project demonstrates the practical application of AI in institutional quality assurance and serves as a scalable solution for governing bodies and educational boards.