Seamless Attendance Logging Application: A Technological Revolution in Logbook Capture

In this age of swift technological progress, Most educational establishments seek novel approaches that may further boost productivity. One such challenge has been the manual attendance tracking system in colleges. This research paper presents the development and implementation of an automated college attendance system powered by deep learning-based text extraction models. The primary objective of this system is to remove the labor-intensive process of taking attendance, improve accuracy, and provide valuable insights into student attendance patterns. The research begins by utilizing state-of-the-art deep learning techniques to extract roll numbers from images of attendance sheets. A Convolutional neural network is utilized for situating and identifying text within images. The system then processes this data and marks students as present or absent based on predefined criteria. The findings demonstrate that the automated attendance system, with its high degree of precision, makes a major dent in the time and energy formerly needed for keeping attendance logs, streamlining the whole process significantly. Furthermore, by enabling the ongoing monitoring and analysis of data in real time, educators and administrators are empowered to make evidence-informed decisions about student attendance through insights continuously gained from observant assessment and evaluation.