Exploring Advances in Meeting Minutes Generation and Face Attendance Systems: A Comprehensive Literature Survey
Prof. Seema Mane1, Anushka Khadtare2, Swamini Hingmire3, Prathamesh Pawar4, Shritej Vasal5
1Department of Information Technology, Sinhgad Institute of Technology and Science
2Department of Information Technology, Sinhgad Institute of Technology and Science
3Department of Information Technology, Sinhgad Institute of Technology and Science
4Department of Information Technology, Sinhgad Institute of Technology and Science
5Department of Information Technology, Sinhgad Institute of Technology and Science
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Abstract - The existing literature survey offers a thorough exploration of automatic text summarization, speech-to-text conversion, and face recognition technologies, all of which are integral to the proposed model named as ConvoLogix. Historically, traditional methods for managing meetings and collaboration involved manual note-taking and attendance tracking. These processes were time-consuming, error-prone, and not conducive to optimization. In today’s fast-paced business environment, the absence of automated solutions hinders efficiency and collaboration. The survey underscores the significance of embracing advanced techniques in Machine Learning with Traditional and Deep Learning Models for Audio and video processing for process automation, emphasizing their pivotal role in streamlining meetings and attendance tracking. A key theme within the survey is the identification of limitations associated with conventional approaches to meeting minute generation and attendance recording. The ConvoLogix model’s core objective is to leverage these technologies to automate meeting minutes generation and attendance tracking, resulting in time savings, improved collaboration, and data-driven insights.
Key Words: Automated Meeting Summarization, Face Attendance Tracking, Natural Language Processing, Machine Learning, Deep Learning, Neural Network Models
