结合古典和神经方法,在基于数学文本的图像中实现更好的细分.
Sakshi1, Chetan Sharma2, Vivek Bhardwaj3
1Amity Institute of Information Technology, Amity University, Noida, India.
Scientific reports
|December 13, 2025
概括
这项研究引入了一个最佳的神经网络,用于分割手写的数学表达式,显著提高识别精度. 新方法的性能优于传统技术,增强了计算机视觉应用.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 数学表达式的识别受到细分挑战的阻碍.
- 现有的研究优先考虑识别而不是细分,特别是在计算机视觉和图像处理方面.
- 手写的数学文本和表达式识别需要强大的细分.
研究的目的:
- 为了解决手写数学表达式识别中的关键细分问题.
- 分析和开发数学表达式的最佳分段解决方案.
- 提高数学表达式识别系统的准确性和稳定性.
主要方法:
- 探索,分类和测试经典细分方法.
- 对各种数学表达式数据集进行比较案例分析.
- 开发和提出一种基于神经网络的最佳细分方法.
主要成果:
- 拟议的神经网络模型在多个数据集 (CROHME,Aidapearson,HasyV) 中实现了竞争性平均交叉与欧盟 (IOU) 分数.
- 绩效指标包括79.4% (CROHME 2014),83.5% (CROHME 2016),81.3% (CROHME 2019),74.6% (Aidapearson) 和79.6% (HasyV) 的表现指标.
- 与传统的细分方法相比,神经网络方法显示出更高的性能.
结论:
- 基于神经网络的细分有效地克服了传统方法的局限性.
- 拟议的解决方案显示了增强数学表达式识别系统的巨大潜力.
- 这项工作推进了对计算机视觉和数学环境中的AI至关重要的细分技术.
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