通过手写稿分析学生考试压力，用AI生成压力指数。
Psychological stress during Examination and its estimation by handwriting in answer script
- 结合光学识别与Transformer模型分析手写笔迹。
- 压力指数基于情感熵融合，准确率超90%。
- 适合教育心理评估与学术诚信检测。
本研究探索将笔迹学与人工智能融合，通过分析学生考试手写答卷来量化心理压力水平。利用光学字符识别和基于Transformer的情感分析模型，提出一种数据驱动方法，突破传统评分体系，深入揭示考试中的认知与情绪状态。系统整合高分辨率图像处理、TrOCR及基于RoBERTa模型的情感熵融合，生成数值化的压力指数。通过五模型投票机制与无监督异常检测提升鲁棒性，为学术鉴证提供创新框架。
原文摘要 · Abstract (English)
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.
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