Psychological stress during Examination and its estimation by handwriting in answer script
This work addresses the need for deeper insights into student cognitive and emotional states in academic settings, representing an incremental advancement by applying existing AI methods to a new domain.
The paper tackled the problem of quantifying psychological stress in students during exams by analyzing handwriting in answer scripts, achieving a robust framework through a five-model voting mechanism and unsupervised anomaly detection.
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.