International Journal of Innovative Research in Computer and Communication Engineering

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TITLE Smart Exam Evaluator
ABSTRACT The evaluation of handwritten descriptive answer sheets is a crucial part of the educational assessment process. Traditional manual checking requires significant time and effort and may result in inconsistencies due to human subjectivity. To address these challenges, this paper presents a Smart Exam Evaluator that integrates Optical Character Recognition (OCR) and Natural Language Processing (NLP) techniques for automated answer assessment. The proposed system converts handwritten responses into digital text using OCR technology. The extracted text is further processed using NLP methods such as text preprocessing, tokenization, normalization, and semantic similarity analysis to understand the meaning and relevance of student answers. The processed response is then compared with predefined model answers to generate an accurate and unbiased score. The experimental results demonstrate that the proposed approach provides reliable evaluation performance while significantly reducing the time required for manual assessment. The system offers a scalable and efficient solution for modern educational environments and can be further improved by incorporating advanced deep learning models and multilingual support.
AUTHOR APARNA MOTE, TANMAY THORAT, ISHWARI WAYDANDE, SHIVAM YADAV, PRAMILA YAMGAR Head of Department, Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India B.E. Students, Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India
VOLUME 185
DOI DOI: 10.15680/IJIRCCE.2026.1406063
PDF pdf/63_Smart Exam Evaluator.pdf
KEYWORDS
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