The cornerstone of a quality education system is Outcome-Based
Education (OBE), which revolves around predefined outcomes or
goals. Based on the best practices of NEP 2020, all the education
institutions and accreditation authorities rely on Outcome-based
teaching, learning, and evaluation. To measure the defined outcome of
each course, it is essential to enter the question-wise marks in the
student exam portal for CO attainment calculation. Traditional
methods adopt the manual entry of marks. This is time-consuming and
error-prone, leading to additional work for each examiner after
evaluation. This project uses computer vision and AI to automate the
identification and calculation of marks from the corrected handwritten
exam answer sheets. It extracts information like roll numbers, question
numbers, and marks, calculates total marks, and exports data to Excel
sheets, hence reduces manual effort and improves efficiency. Deep
neural network-based pre-trained models are used for the effective
identification of handwritten marks. We have trained and tested the
proposed system through continuous assessment and internal
examination answer sheets available in our institution.
J. Annrose, A. Shibin Sam, S. Samnath, Jennings Rousseau Raj, I.R. Shanjeya Bhaarath
St. Xavier’s Catholic College of Engineering, India
Handwritten Digit Recognition, Outcome Based Education (OBE), Measuring Outcomes, Attainment Calculation, Computer Vision, Deep Learning
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 15 , Issue: 4 , Pages: 3704 - 3708 )
Date of Publication :
January 2025
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