• Suraj BM
• Ameer Sawad
• Arafath Ali
• Parvatraj KMM
• Daya Naik

This mini-project presents an OMR (Optical Mark Recognition) Sheet Evaluation System developed using Django for the backend and HTML/CSS for the frontend, designed to automate the process of evaluating multiple-choice examination sheets. The system enables users to either upload scanned OMR sheets or capture them directly using a camera. Once uploaded, the system preprocesses the image using image processing techniques such as grayscale conversion, thresholding, and contour detection. A fixed template layout is used to detect the answer regions on the sheet. The system uses OpenCV to segment these regions, identify filled bubbles based on pixel intensity analysis, and extract the selected answers. The extracted data is then compared against a predefined answer key to compute scores, which are saved in a Django-based database for record keeping and further analysis. This automated solution not only reduces the manual workload of evaluating exams but also improves accuracy and efficiency. The modular design of the system makes it extensible for future integration with a React frontend or a mobile app interface.

OMR Evaluation, Django, Image Processing, Sheet Scanning, Education Technology, Python, Web Application, Automated Scoring

"OMR Sheet Evaluation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.690-693, July-2025, Available :http://www.jetir.org/papers/JETIRGX06130.pdf

"OMR Sheet Evaluation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp690-693, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06130.pdf