卷积神经网络 (CNN) 基于印度旁遮普邦的古鲁木希文字的月份名称识别
Tajinder Pal Singh1, Sheifali Gupta1, Jamil Hussain2
1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
PeerJ. Computer science
|February 3, 2025
概括
本研究引入了一个卷积神经网络 (CNN) 模型,用于自动 Gurumukhi 文本识别. 开发的系统在分类古鲁木吉月份方面取得了很高的准确性,解决了印度语言数据处理方面的挑战.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动化数据分析和解释系统对自然语言识别有很高的需求.
- 为印度开发这样的系统是具有挑战性的,因为它的语言多样性,包括多种脚本和语言.
- 在旁遮普邦使用的古鲁穆基文字,由于其复杂的字符结构,为自动文本识别带来了独特的挑战.
研究的目的:
- 为古鲁木吉文本设计一个无错误的分类模型.
- 开发一个全方位的 Gurumukhi 几个月的词识别系统.
- 为解决 Gurumukhi 语言的自动文本识别的复杂性.
主要方法:
- 设计了一个基于卷积神经网络 (CNN) 的分类模型.
- 该模型被训练为Gurumukhi几个月的整体词识别.
- 编制了一套由500位作家贡献的24000个Gurumukhi月份的24,000个文字图像的数据集.
主要成果:
- 拟议的CNN模型在分类古鲁穆基月份方面取得了很高的准确性.
- 在验证数据上获得的最高准确率达到99.7%.
- 该研究成功地展示了Gurumukhi文本识别的有效方法.
结论:
- 开发的CNN模型对于准确的古鲁木吉文本分类是有效的.
- 这项工作为印度语言的自动识别系统提供了重大进展.
- 拟议的方法提供了一个强大的解决方案来识别Gurumukhi脚本,克服以前的挑战.
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