Objective Script Evaluator
MeeraPrabhaja Kumari, S. Dhara Vivek, G. Hasith Anjan, Paseddula Chandrasekhar, G. Jeevana Manikanta, K. Prasanna Lakshmi · 2025
The goal is to introduces a method to automate the evaluation of single page handwritten objective scripts, addressing the time-in taken evaluation and excessive costs associated with conventional strategies. By using Optical Character Recognition (OCR) with advanced Convolutional Neural Networks (CNNs), handwritten responses are efficiently captured, processed, and evaluated. The system then compares diagnosed characters to an unique key, systematically storing student marks for organized results in a .CSV file. By bypassing the need for high priced Optical Mark Recognition (OMR) sheets and by the usage of the Handwritten characters Dataset from Kaggle as the medium for training and validation. The proposed technique validates its usefulness for automated grading and data extraction applications by exhibiting robust performance in assessing real-world handwritten responses and a remarkable accuracy of over 94% this method also significantly reduces setup and maintenance expenses, providing a budget friendly opportunity for evaluation of single page objective scripts.