Personal mark density-based high-performance Optical Mark Recognition (OMR) system using K-means clustering algorithm

To evaluate multiple choice question tests, optical forms are commonly used for large-scale exams and these forms are read by the OMR (Optical Mark Recognition) scanners. However, OMR scanners often misinterpret marks that have not been fully erased, which can lead to incorrect readings. To overcome that shortcoming and reduce the time and labor lost in the assessment process, we developed a novel system based on the density of each individual’s markings, providing a more personalized and accurate approach. Instead of reading according to a specific optical form template, a dynamic and flexible structure was generated where users can create own templates and obtain the model that reads according to that template. We also optimized certain aspects of the system for efficiency, such as image memory transfer and QR code reading. These optimizations significantly increase the performance of the OMR scanners. One of the key issues addressed is inaccurate reading of OMR scanners when a student doesn’t fully erase their markings or when markings are faint. After the scanning process, the proposed approach uses a K-means clustering algorithm to classify different density markings. This technique identifies each student’s personal marking density, enabling a more accurate interpretation of their responses. According to the experimental results, we performed 97.7% improvement compared to the misread optics scanned by the conventional OMR devices. In tests performed on 265.816 optical forms, we obtained an accuracy rate of 99.98% and a reading time of 0.12 seconds per optical form.