Authors - Deepak Mane, Siddhi Dhamal, Shivam Devkar, Divit Maheshwari, Riddhi Kaulage, Diya Nair, Deepak R. More Abstract - The evaluation of handwritten answers sheet has so many challenges since from many years due to variability in handwriting, linguistic barrier and personal bias. This is very time-consuming method and inconsistent method which highlights the need for automated subjective answers evaluation. Here, proposed automated handwritten answers evaluation system uses TrOCR based handwritten answer detection, NLTK tokenization, WordNet lemmatization and semantic similarity check between teacher’s and student’s answer based on meaning. This advanced multi-model system overcomes traditional keyword matching technique and improves contextual accuracy. This system also overcomes traditional manual checking and results in fast evaluation. The system promotes the fairness, fast and accurate processing. Moreover, the suggested framework removes human fatigue, encourages fair grading, and offers a solution that can be used for large-scale academic tests. The results show that this automated method not only works like a human brain but also makes the evaluation process more fair and open.0
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