Psychological stress during Examination and its estimation by handwriting in answer script
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915619116417024 |
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| author | Kumar, Abhijeet Agarwal, Chetan Neogi, Pronoy B. Goswami, Mayank |
| author_facet | Kumar, Abhijeet Agarwal, Chetan Neogi, Pronoy B. Goswami, Mayank |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11633 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Psychological stress during Examination and its estimation by handwriting in answer script Kumar, Abhijeet Agarwal, Chetan Neogi, Pronoy B. Goswami, Mayank Computer Vision and Pattern Recognition 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. |
| title | Psychological stress during Examination and its estimation by handwriting in answer script |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.11633 |