在智能手机中使用主动学习框架进行LFS-手写质量评估
K S Koushik1, B J Bipin Nair2, N Shobha Rani3
1Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India.
Scientific reports
|February 10, 2026
概括
本研究引入了一种回归模型,用各种特征来评估手写质量. 该方法实现了高准确度,证明了其在评估手稿整洁性和可读性方面的有效性.
科学领域:
- 计算机科学 计算机科学
- 法医科学 法医科学 法医科学
背景情况:
- 评估手写质量对于各种应用,包括法医分析和教育评估至关重要.
- 现有的方法往往缺乏客观和定量措施来评估字层级的手写.
研究的目的:
- 开发和评估一种基于回归的方法,以评估字层面的手写质量.
- 识别影响手写质量的关键特征,并比较不同的机器学习模型.
主要方法:
- 利用了来自65多名作家的1296多个无规则和1160个规则的手写样本.
- 使用结构,感知和边缘特征进行分析.
- 训练并比较了各种机器学习模型,包括Random Forest和XGBoost,这些模型包含了积极学习策略.
主要成果:
- 随机森林和XGBoost模型实现了0.996.996.99的卓越的R平方值.
- 感知属性 (整洁性,可读性) 是最有影响力的特征.
- 积极学习通过有效地选择不确定的样本而不是随机抽样,增强了模型培训.
结论:
- 开发的基于回归的方法准确地评估文字级手写质量.
- 这些发现强调了感知特征的重要性以及积极学习在模型改进中的有效性.
- 该方法为分析手写特征和识别低质量的手稿提供了强大的工具.
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