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相关概念视频

Classification of Systems-II01:31

Classification of Systems-II

141
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
141

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相关实验视频

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基于深度学习的帕什托手写文本识别系统:PHTI的基准.

Ibrar Hussain1,2, Riaz Ahmad1, Khalil Ullah3

  • 1Department of Computer Science, Shaheed Benazir Bhutto University, Sheringel, Dir, Pakistan.

PeerJ. Computer science
|April 25, 2024
PubMed
概括

本研究介绍了使用多维长期短期记忆 (MD-LSTM) 网络识别普什图语手写文本的第一个基线系统. 该系统实现了20.77%的字符错误率 (CER),为普什图语数字化过渡提供了洞察力.

关键词:
深度学习是一种深度学习.自然语言处理自然语言处理.这是光学字符识别系统.巴什图语手写文本图像库

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科学领域:

  • 自然语言处理自然语言处理.
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 普什图语缺乏用于手写文本识别的基线系统.
  • 语言的数字化转型需要强大的识别系统.

研究的目的:

  • 为普什图语手写文本引入第一个基线识别系统.
  • 评估一个多维长期短期记忆 (MD-LSTM) 网络在Pashto手写文本图像库 (PHTI) 数据集上的性能.

主要方法:

  • 预处理PHTI数据集以删除不需要的字符.
  • 开发一种利用多维长短期记忆 (MD-LSTM) 网络的识别系统.
  • 经验分析以优化MD-LSTM参数和对最先进模型进行比较实验.

主要成果:

  • 拟议的MD-LSTM系统在PHTI测试套件上实现了20.77%的基线字符错误率 (CER).
  • 探索了隐藏层大小 (10,20,80) 和Tanh层大小 (20,40) 的新奇性.
  • 分析了前20个困惑点,以了解模型的局限性.

结论:

  • 开发的系统为普什图语手写文本识别奠定了基础.
  • 结果表明普什图语数字化转型面临的挑战和未来方向.
  • 需要进一步的研究来提高识别精度和解决特定语言的复杂性.