一个新的语言独立的深度CNN用于现场文本检测和社交媒体图像中的风格转移
概括
本研究介绍了一种端到端的模型,用于检测文本并将其风格转移到社交媒体图像中. 这种新的方法提高了图像质量和字符恢复,以提高多语言和跨语言的性能.
科学领域:
- 计算机视觉 计算机视觉
- 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:
- 社交媒体图像经常受到质量下降和语言多样化的困扰,这使得文本检测和风格转移具有挑战性.
- 现有的方法与现实世界的社交媒体图像数据的复杂性作斗争.
研究的目的:
- 开发一种新的端到端模型,用于在社交媒体图像中进行强大的文本检测和风格传输.
- 解决图像质量和自然场景文本中任意语言的不利影响.
主要方法:
- 使用频域梯度提取技术来识别文本候选点,减少图像质量问题.
- 文本检测是使用一个UNet++网络与EfficientNet骨干 (EffiUNet++) 进行的.
- 有剩余映射和注意模块的生成模型 (TESP-Net) 处理文本风格转移,利用识别结果.
主要成果:
- 与现有方法相比,拟议的模型在文本检测和风格转移方面表现出卓越的性能.
- 社交媒体和基准数据集的实验表明,在多语言和跨语言场景中有效.
- 该模型成功地从损坏的图像中恢复了字符结构和细节.
结论:
- 开发的端到端模型在具有挑战性的社交媒体环境中为文本检测和风格传输提供了重大进步.
- 频域方法和生成模型有效地处理图像质量下降和风格变化.
- 该方法显示了对于需要在各种语言和图像类型中准确识别和操纵文本的应用程序的巨大潜力.
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