普什托语 手写不变字符轨迹预测 使用定制深度学习技术
Fazli Khaliq1, Muhammad Shabir2, Inayat Khan3
1Department of Computer Science, Islamia College University Peshawar, Peshawar 25000, Pakistan.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究开发了一种深度学习模型,用于识别巴什图语的手写字符和联字符,这对于保护语言至关重要. 一个定制的CNN实现了超过92%的准确性,优于其他模型.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 历史文件和沟通在很大程度上依赖于跨越不同种族群体的手写记录.
- 手写文本识别的自动化工作主要集中在主要语言上,忽略了区域和小语言.
- 巴什图语是巴基斯坦和阿富汗至关重要的斜体文字,需要通过自动识别来保存.
研究的目的:
- 开发和评估深度学习模型,用于检测和识别巴什图语手写字符和连字符.
- 通过数据增强和降噪技术增强普什图语手写数据集.
- 为了比较各种深度学习架构在普什图文字识别方面的性能.
主要方法:
- 创建了一个新的巴什图语手写字符和连字符的数据集,并使用缩放和旋转来增强和增强.
- 用于数据预处理和消除噪音,应用了形态操作和中间选.
- 训练和评估了几种深度学习模型,包括VGG19,MobileNetV2,MobileNetV3和定制的卷积神经网络 (CNN).
主要成果:
- 定制的CNN模型在评估的深度学习技术中实现了最高的准确性和最低的损失.
- 定制的CNN模型实现了93.98%的训练精度,92.08%的验证精度和92.99%的测试精度.
- 数据增强和预处理技术显著提高了识别模型的稳定性.
结论:
- 深度学习,特别是定制的CNN,对于识别普什图语手写字符和联字符是有效的.
- 开发的方法有助于普什图语的保存和可访问性.
- 这项研究强调了将先进的人工智能技术应用于资源不足的语言的潜力.
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