联合变化和ZhuYin数据集用于传统中文文档增强
Shi-Wei Lo1, Hsiu-Mei Chou2, Jyh-Horng Wu2
1National Center for High-Performance Computing, Hsinchu, Taiwan. LSW@narlabs.org.tw.
Scientific data
|November 28, 2024
概括
一个新的数据集,联合变化和朱 (JVZY),解决了传统中文文档增强培训数据的稀缺问题. 它具有20,000张具有多种降解的图像,有助于人工智能开发.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数字图像处理 数字图像处理
背景情况:
- 数字文档质量对于信息管理至关重要,但往往因注释,扭曲和污染而退化.
- 深度学习方法增强了文档,但需要广泛的,高质量的数据集进行培训和评估.
- 现有的基准数据集很少,特别是对于传统中国文献,这阻碍了这一领域的进步.
研究的目的:
- 引入一个新的,大规模的数据集,用于传统中文文档增强.
- 提供一个资源,解决传统中国语音符号和文档退化所面临的具体挑战.
- 促进先进的人工智能应用程序的开发,以增强退化的传统中文文档.
主要方法:
- 创建联合变异和朱 (JVZY) 数据集.
- 包括2万张图像和192万个单词.
- 整合了各种文档退化特征和独特的传统中文语音符号.
主要成果:
- JVZY数据集为传统中文文档增强提供了全面的资源.
- 它包含了广泛的降解类型和特定的语言特征.
- 数据集被设计成一个不断变化的资源.
结论:
- JVZY数据集填补了传统中文文档增强资源的关键缺口.
- 它将加速对该领域人工智能模型的研究和开发.
- 此资源支持创建更有效的文档增强应用程序.
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