A Web-Based Automated OMR Evaluation System Using YOLO and Image Processing Techniques

Optical Mark Recognition (OMR) is widely used in large-scale examinations for fast and reliable evaluation of objective-type answer sheets. Traditional OMR systems rely heavily on fixed templates and specialized scanning hardware, making them costly, inflexible, and sensitive to misalignment and image distortions. This paper presents a web-based Automated OMR Evaluation System that integrates deep learning–based object detection with classical image processing techniques to achieve accurate, scalable, and hardware-independent evaluation. The proposed system employs a fine-tuned YOLO-based object detection model to locate answer regions on OMR sheets, followed by deterministic segmentation and bubble analysis using OpenCV. The system supports OMR images captured using ordinary mobile cameras or scanners and remains robust under varying lighting conditions, rotations, and marking styles. Subject-wise classification, visual confirmation outputs, and structured JSON results enhance transparency and usability. Experimental results demonstrate high accuracy (up to 98–99%), low processing time (2–4 seconds per sheet), and significant improvements over manual evaluation. The proposed solution offers a practical and scalable alternative to conventional OMR systems for academic institutions and examination bodies.