显示语义转换器用于场景文本识别
Xinqi Yang1,2,3, Wushour Silamu1,2,3, Miaomiao Xu1,2,3
1College of Computer Science and Technology, Xinjiang University, No. 777 Huarui Street, Urumqi 830017, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了显示语义转换器 (DST),通过整合语言知识来改善场景文本识别. DST通过语义信息增强视觉特征,提高准确性和稳定性,特别是在噪音较大的图像中.
科学领域:
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 场景文本识别模型经常因缺乏语言理解而与杂的图像作斗争.
- 现有的视觉模型只关注视觉纹理,忽视语义上下文.
- 这种限制导致在具有挑战性的视觉条件下识别精度降低.
研究的目的:
- 通过整合语言和视觉信息来增强场景文本识别.
- 提高模型对抗图像噪声和扭曲的稳定性.
- 开发一个更准确,更有效的场景文本识别系统.
主要方法:
- 在Vision Transformer架构的基础上构建.
- 介绍显示语义变压器 (DST) 模型.
- 整合了一个面具语言模型和一个语义视觉交互模块.
主要成果:
- DST模型显著提高了基准数据集的识别精度,平均精度高出近2%.
- 语义视觉交互模块有效地通过深层次的语义信息来增强视觉特征.
- 该模型在噪音图像中识别文本方面表现出更好的稳定性.
结论:
- 显示语义转换器 (DST) 通过有效地结合视觉和语义信息,为场景文本识别提供了卓越的方法.
- 通过小的参数数量,DST在准确性和推断速度之间实现了更好的平衡.
- 这种方法提高了模型的稳定性和识别性能,特别是在具有挑战性的现实场景中.
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