Smart Hackathon Management System with Machine Learning Integration

In the rapidly growing landscape of hackathons and competitive coding events, managing large-scale participation and evaluating diverse submissions have become increasingly challenging. Traditional methods of handling hackathons are often inefficient, particularly in assessing and summarizing large volumes of participant data, presentations, and project documentation. This paper introduces a Smart Hackathon Management System that integrates advanced machine learning techniques to address these challenges, focusing on text summarization using seq2seq models and image-based text extraction. The proposed system automates the summarization of textual content, such as project proposals and idea presentations, allowing juries to make quick, informed decisions. Additionally, it incorporates a robust image text extraction module, designed to accurately retrieve and analyze text from images, further enhancing the system's capability to handle various forms of data. This integration of machine learning models within a hackathon management platform not only streamlines the evaluation process but also improves accuracy and reduces the manual workload involved in event management. The effectiveness of the proposed system is validated through extensive experiments, highlighting its potential for broader applications in event management and other domains requiring efficient handling of large datasets.