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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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一个 Pix2Pix 架构,用于完整的线下手写文本规范化.

Alvaro Barreiro-Garrido1, Victoria Ruiz-Parrado1, A Belen Moreno1

  • 1Higher Technical School of Computer Engineering, Universidad Rey Juan Carlos, c/Tulipan sn, Mostoles, 28922 Madrid, Spain.

Sensors (Basel, Switzerland)
|June 27, 2024
PubMed
概括

本研究介绍了一种Pix2Pix模型,用于规范手写文本图像,改善线下手写文本识别. 这种可训练的方法与深度学习模型无集成,匹配或超过启发式方法.

关键词:
这是一把手枪.IAM 数据集 IAM 数据集深度学习是一种深度学习.图像规范化的图像规范化离线手写方式 离线手写像素2pixxxx 在线观看扫描文本预处理 预处理

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 离线手写文本识别依赖于预处理规范化算法.
  • 现有的方法经常使用未与识别模型集成的启发式策略.
  • 这限制了标准化和识别组件的统一训练.

研究的目的:

  • 介绍一个Pix2Pix可训练的模型来规范手写文本图像.
  • 为了使正常化能够无集成,作为深度学习识别架构的初始阶段.
  • 为了促进标准化和识别的统一培训,同时保持模块的可解释性.

主要方法:

  • 使用Pix2Pix条件生成对抗网络进行图像规范化.
  • 在混合启发式转换上训练模型,以解决手写的变化.
  • 整合了规范化方法作为深度识别架构的第一步.

主要成果:

  • 实现了斜坡和斜坡的正常化,以及正常化的上升/下降尺寸.
  • 提出的方法复制,在某些情况下,超过了广泛使用的启发式算法.
  • 当集成到深度识别架构中时,在两个指标上表现出有效性.

结论:

  • Pix2Pix模型为手写文本规范化提供了一种有效的,综合的方法.
  • 这种方法减轻了人与人之间的手写变化,提高了识别性能.
  • 可训练的规范化是传统启发式预处理技术的可行替代方案.