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@article{176150,
        author = {Rushikesh G. Dongare and Prof. Sneha Suryavanshi and Gauri Y. Kakulte and Manjiri M. Tamkar and Shraddha S. Tambekar},
        title = {Automated OMR Evaluation System with Integrated Feedback and Performance Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4971-4975},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176150},
        abstract = {Manual evaluation of Optical Mark Recognition (OMR) sheets is a significant bottleneck in modern education, characterized by inefficiency, potential for human error, and delays in providing feedback. This paper presents the design, development, and evaluation of an intelligent, full-stack exam evaluation system that integrates image processing, machine learning, and Artificial Intelligence to overcome these challenges. The system automates the assessment of multiple-choice questions by accurately scanning and evaluating OMR sheets using a custom engine powered by OpenCV and ML classifiers. A key innovation is the inclusion of a custom OCR pipeline, leveraging OpenCV and Tesseract, to extract questions directly from handwritten papers, eliminating tedious manual data entry. Furthermore, the system utilizes Large Language Models (LLMs), such as Google Gemini to generate personalized, AI-driven feedback for students, offering insights into their strengths, weaknesses, and targeted study suggestions.},
        keywords = {Automated evaluation, Image processing, Machine learning, Optical mark recognition, Large language models.},
        month = {April},
        }