Automated OMR Analyser using ML and Image Processing

Abstract

This work explores Optical Mark Recognition (OMR) systems built on Machine Learning and Image Processing techniques to achieve accurate and automated evaluation of answer sheets. The primary objective is to eliminate dependency on specialized scanners by utilizing devices such as mobile cameras and webcams. This system processes scanned answer sheets by isolating responses and matching them with reference answers using automated logic. Several models integrate contour detection, circle detection, and error-tolerant classification to ensure robustness even with imperfect markings. The survey shows that OMR systems deliver high accuracy in diverse lighting and marking conditions. The findings support the development of scalable and cost-effective OMR solutions that are particularly useful in academic and institutional settings.

Introduction

Traditional Optical Mark Recognition (OMR) systems for grading multiple-choice answer sheets rely on specialized scanners and strict formats, which limit flexibility and increase costs. Advances in Image Processing and Machine Learning have enabled more adaptive, cost-effective OMR solutions that can use ordinary cameras or smartphones for scanning, making them accessible to resource-limited institutions.

Key technologies involved:

• Image Processing methods (filtering, thresholding, contour detection) prepare and segment answer sheets under varied lighting and orientations.

• Machine Learning models classify filled versus unfilled bubbles, detect anomalies (e.g., multiple or faint marks), and improve accuracy.

Recent research highlights:

• Systems using grayscale conversion, adaptive thresholding, and contour detection for bubble isolation, eliminating need for expensive hardware.

• Approaches integrating ML classifiers that handle partial or misaligned markings, boosting reliability.

• Solutions focused on robustness against noise, lighting changes, and imperfect scanning conditions.

• Some use advanced deep learning (CNNs, Bi-directional Associative Memory networks) for fast, fault-tolerant, and scalable scoring.

• Emphasis on accessibility, enabling scanning with webcams or smartphones and minimizing hardware dependence.

Overall, modern OMR systems increasingly combine IP and ML techniques to deliver flexible, accurate, and efficient automated grading, reducing manual effort and expanding usability in diverse educational environments.

Conclusion

The paper reveals significant progress in OMR systems, with growing adoption of machine learning and deep learning techniques. While recent models incorporate CNN and real-time image analysis. Open-source tools like OpenCV, TensorFlow, have further accelerated innovation in this domain. The surveyed studies emphasize accuracy, robustness, and affordability—especially in education and assessment contexts. However, challenges such as noise, overlapping marks, and real-time processing remain areas for continued research. Notably, webcam-based and mobile-friendly solutions make OMR more accessible. The insights from related works have shaped the direction of our system, validating its practical potential. Overall, the integration of ML and Image Processing approaches is transforming OMR into a scalable, intelligent, and efficient solution.

References

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Copyright

Copyright © 2025 Ravindra S, Ranganath K N, Nithin A, Manoj M H, M S Hemanth Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.