Psychological stress during Examination and its estimation by handwriting in answer script

TL;DR

This paper introduces an AI-driven method combining graphology and sentiment analysis to quantify students' psychological stress during exams through handwriting analysis, providing a novel, data-driven stress assessment tool.

Contribution

It presents a new framework integrating OCR and transformer-based sentiment analysis to estimate stress from handwritten scripts, surpassing traditional grading methods.

Findings

Achieved a robust Stress Index with a five-model voting system.

Integrated high-resolution image processing with sentiment entropy fusion.

Demonstrated effectiveness of the approach in academic forensics.

Abstract

This research explores the fusion of graphology and artificial intelligence to quantify psychological stress levels in students by analyzing their handwritten examination scripts. By leveraging Optical Character Recognition and transformer based sentiment analysis models, we present a data driven approach that transcends traditional grading systems, offering deeper insights into cognitive and emotional states during examinations. The system integrates high resolution image processing, TrOCR, and sentiment entropy fusion using RoBERTa based models to generate a numerical Stress Index. Our method achieves robustness through a five model voting mechanism and unsupervised anomaly detection, making it an innovative framework in academic forensics.