• Aditya Hiremath
• Nipun Irabatti
• Prabhuraj Dhondge
• Akhilesh Desai
• Shagufta Sheikh

This paper proposes an automated system for grading handwritten subjective answers, leveraging advanced computer vision, natural language processing, and large language model techniques. Although time-consuming, the system presents a promising theoretical approach by employing CRAFT for text detection, TrOCR for handwritten text recognition, and a fine-tuned language model for answer evaluation. Experimental results demonstrate the system’s potential accuracy in transcribing handwritten text and consistency in grading answers compared to human raters. The proposed methodology offers a scalable and efficient solution to automate the traditionally labor-intensive task of grading handwritten responses, with the potential to transform education assessment practices. The system’s performance, limitations, and future research directions to improve efficiency are discussed.

Handwritten answer grading, text detection, optical character recognition, natural language processing, language models, transformer models

"Transforming Handwritten Answer Assessment: A Multi-Modal Approach Combining Text Detection, Handwriting Recognition, and Language Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.b531-b538, June-2024, Available :http://www.jetir.org/papers/JETIR2406163.pdf

"Transforming Handwritten Answer Assessment: A Multi-Modal Approach Combining Text Detection, Handwriting Recognition, and Language Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppb531-b538, June-2024, Available at : http://www.jetir.org/papers/JETIR2406163.pdf