SmartExam: A Deep Learning-Driven Approach for Structured Extraction of Examination Data from Unstructured Documents

Abstract

The management of examination-related activities in academic institutions involves handling large volumes of unstructured documents, including timetables, hall plans, duty lists, and examiner assignments. Conventional approaches rely on manual preparation and dissemination of such information, leading to inefficiencies, delays, and increased risk of human error. Limited accessibility, lack of structured data storage, and absence of personalized information delivery further complicate the examination management process. This work presents an intelligent examination management system designed to automate the extraction and organization of examination information from unstructured documents. The proposed framework processes uploaded documents and identifies key entities such as examination dates, subject details, schedules, hall allocations, and duty assignments. Extracted information is transformed into a structured format and stored in a centralized database, enabling efficient retrieval and role-based access. The system supports secure authentication mechanisms and provides personalized access to examination data for students and faculty members. Automated processing reduces manual workload, minimizes errors, and ensures consistency in information delivery. The framework enhances transparency and communication efficiency by providing accurate and timely access to examination schedules and related data. Experimental evaluation demonstrates high accuracy in extracting relevant entities from diverse document formats, confirming the effectiveness of the proposed approach. The system offers a scalable and reliable solution for modernizing examination management processes, contributing to improved operational efficiency and user experience in academic institutions.