Implementasi Optical Mark Recognition dengan Pendekatan Template-Based untuk Koreksi Otomatis Lembar Jawaban Pilihan Ganda
DOI:
https://doi.org/10.47065/bulletincsr.v6i6.1356Keywords:
Optical Mark Recognition; Computer Vision; Computer Answer Sheet; Image Processing; Automatic CorrectionAbstract
Manual checking of multiple-choice answer sheets is time-consuming and labor-intensive, particularly when a large number of answer sheets need to be examined. Optical Mark Recognition (OMR) can be used to automate the image-based reading and correction of answer sheets. This study implements a computer vision-based OMR system that utilizes a blank answer sheet template as a reference for identifying mark changes in answer-choice areas. The system was developed using the Python programming language and the OpenCV library through several stages, including preprocessing, grayscale conversion, adaptive thresholding, contour detection, perspective correction, image alignment with the template, answer-area segmentation, answer identification, answer correction based on the answer key, and score calculation. The system was designed to recognize six output classes: A, B, C, D, E, and BLANK. Evaluation was conducted using 50 answer-sheet images, each containing 60 questions with five answer alternatives, resulting in 3,000 answer decisions and 15,000 answer-choice areas being analyzed. All 50 answer sheets were successfully processed during the validation stage. The evaluation results showed that 2,863 predictions were correctly identified and 137 predictions were incorrect, yielding an accuracy of 95.43%, macro precision of 95.62%, macro recall of 94.53%, and macro F1-score of 94.92%. Specific evaluation of the BLANK class achieved a precision of 100%, recall of 85.71%, and F1-score of 92.31%. These results indicate that the computer vision- and template-based OMR approach can be used to automatically read and correct answer sheets with good performance. Nevertheless, 18 answers that were actually blank were identified as one of the A–E options. This condition indicates that visual variations in answer areas can still affect the identification process; therefore, optimization of the template-comparison mechanism and testing under more diverse image conditions are required to improve the robustness of the system.
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