Optical Mark Recognition (OMR) is the automated process of capturing the data which is in the form of bubbles, squares or tick marks. This technique is widely used in various applications like exam evaluation, automated attendance marking, voting and community surveys etc.
Though the technique usually makes use of commercially available dedicated OMR scanners, but it has its own drawbacks. The present work proposes to automate the same using machine vision for exam evaluation.
A standardized sheet is designed for conducting any type of exam. Special marks on the sheet ensure the sheet is not skewed or folded. Every mark on the sheet is recognized using the unique alphanumeric character assigned to it. This is done by pattern matching in Machine Vision Assistant 7.1 and LabVIEW 7.1. The accuracy attained by the system for 100 samples is 98.45%.
In today’s era when life has become tremendously fast, man is becoming highly dependent on automated machines. And in the process of automation, the very much talked of machine vision has its own significance. Machine Vision (MV) is a subfield of artificial intelligence wherein the power of vision is imparted to the machines.
Machine vision has been defined by the Machine Vision Association of the Society of Manufacturing Engineers and the Automated Imaging Association as “The use of devices for optical, non-contact sensing to automatically receive and interpret an image of a real scene in order to obtain information and/or control machines or process”
Source: Thapar University
Author: Shruti Bansal