Optical Mark Recognition (OMR) is a widely-used method for automatically capturing data from human-marked documents such as tests, surveys, and assessments. This paper presents a technique for OMR detection leveraging image processing methods. Traditional OMR systems rely on specialized hardware; however, with advancements in computer vision, we propose a solution that utilizes common digital imaging devices like scanners or cameras, making the process more accessible and cost-effective. The technique involves pre-processing scanned or captured images to enhance clarity, followed by feature extraction methods to detect and interpret marked areas accurately. Key image processing steps, such as grayscale conversion, noise reduction, edge detection, and contour identification, are employed to locate and analyze marked fields. Furthermore, our approach incorporates algorithms to handle skew, uneven lighting, and other imperfections in image quality. This method demonstrates high accuracy in detecting marks even in suboptimal conditions and offers flexibility across different form designs. The proposed system can be integrated into existing digital frameworks, reducing dependency on traditional OMR scanners while maintaining precision and efficiency.