• SHAKE ABDUL RUB
• Y. VAMSI KRISHANA SAI
• D. VENKATESH
• B. TEJA
• P. SUBBA RAJU

In today's era of automation, there is a growing need for automating the evaluation of hand-written MCQs (Multiple Choice Questions) answer sheets. The traditional method of manually checking these answer sheets is time-consuming and requires a significant amount of manpower. The aim of this journal is to introduce an MCQ-CNN that uses deep learning to automatically evaluate hand-written MCQs answer sheets, thereby reducing the need for human intervention and saving time. By inputting the answer key into the application, the system can evaluate the answers provided by students and allocate marks accordingly. This automated approach can help reduce the workload of teachers and make the evaluation process more efficient. With the increasing popularity of online learning and the need for fast and accurate evaluations, this application can be a valuable tool for educational institutions.

The assessment of multiple-choice questions, the extraction of handwritten text, and the utilization of deep learning algorithms.

"BIT PAPER EVALUATION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.g318-g321, July-2023, Available :http://www.jetir.org/papers/JETIR2307649.pdf

"BIT PAPER EVALUATION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppg318-g321, July-2023, Available at : http://www.jetir.org/papers/JETIR2307649.pdf