An Enhanced Fault Tolerance Algorithm for Optical Mark Recognition Using Smartphone Cameras

Optical Mark Recognition (OMR) systems have been studied since the 1970s. Due to its simplicity of use and efficiency in bulk operations, OMR technology has been gaining popularity over time. They are used as an automated data input technique for surveys and multiple-choice question papers in educational institutions for automatic evaluation and grading of student inputs. The requirement of the conventional OMR systems comprises specialized OMR machines or optical scanners with automatic document feeding capability. These machines and scanners are fixed-location devices and cannot be moved easily. Their energy requirements are high, while they also require human efforts to operate. These machines are expensive and, hence pose budget constraints for small educational institutions. Due to being mechanical, their maintenance and operating cost is high. To overcome these limitations, alternate devices are smartphone cameras, which though handy adversely lack the capability of scanning documents in a controlled environment. An uncontrolled environment leads to inputs that existing OMR algorithms do not recognize at large, while the accuracy rate and precision stay low to an undesirable extent. Due to this shortcoming, the usage of smartphone cameras is still not feasible. In this experimental study, we have proposed an OMR algorithm specifically for inputs taken from smartphones equipped with decent cameras and running Android or iOS operating systems. Thus effectively, we have ported the OMR technology to smartphones, offering more flexibility, easiness, and mobility of its usage in daily life. The key issue that transpired in our experiments is the bad illumination in different lighting conditions. Our results are very promising and comparable to those obtained from the usage of optical scanners.