基于多尺度融合CRNN的文本识别模型
Le Zou1, Zhihuang He1, Kai Wang1
1School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种新的场景文本识别模型,该模型通过多尺度融合增强了特征提取. 与传统方法相比,改进的模型通过捕捉更完整的字符特征来实现更高的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 场景文本识别在计算机视觉中至关重要.
- 目前的模型由于有限的下采样尺度而难以进行不完整的特征提取.
- 这导致识别图像中的文本的准确性降低.
研究的目的:
- 提出一个新的场景文本识别模型,解决不完整的特征提取.
- 通过增强功能完整性来提高文本识别准确性.
- 在卷积循环神经网络 (CRNN) 框架内利用多尺度特征融合.
主要方法:
- 提出了一个集结卷积,特征融合,递归和转录层的新模型.
- 卷积层采用双尺度特征提取.
- 一个特征融合层结合了多个尺度的特征,其次是用于上下文学习的反复层.
主要成果:
- 拟议的模型扩大了识别领域,并在多个尺度上学习特征.
- 它提取了更完整的字符特征,从而改善了文本识别.
- 实验结果显示,在各种场景文本数据集上,标准CRNN模型的性能优于标准CRNN模型.
结论:
- 新的多尺度融合CRNN模型显著提高了场景文本识别的准确性.
- 这种方法有效地克服了现有方法中不完整的特征提取的局限性.
- 该模型在各种现实世界场景文本数据集中展示了强大的性能.
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