.png){kind=icon}
返回
Self-supervised memory learning for scene text image super-resolution
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2024.125247.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Computerised recognition of low-resolution scene text images has been a persistent challenge. To improve the recognition performance, image quality enhancement via image super-resolution technology provides an intuitive solution. Typical deep learning-based scene text image super-resolution methods assume that the image quality degradation from high-resolution images to their corresponding low-resolution counterparts can be represented by mapping well-distributed samples, which limits their reconstruction performance in a practical text recognition system. For real-world scenarios this assumption typically does not hold since image degradations arise from multiple sources during image capture and processing. In this paper, to alleviate this problem, we propose a novel self-supervised end-to-end memory network model for scene text image super- resolution. In particular, after extracting enriched and finer representations from low-resolution text images via a spatial refinement block, we introduce a memory-based network to yield an improved super-resolution model that can handle complex degradation sources. Furthermore, to boost the effectiveness of our method, we design a multi-term loss to exploit textual structure information, where, in addition to the traditional reconstruction loss, we embed a character perceptual loss and a boundary enhancement loss. Extensive experiments on different datasets demonstrate that our proposed MNTSR method effectively improves the recognition accuracy for several scene text image recognition models and achieves state-of-the-art results. The source code is made available at https://github.com/xyzhu1/MNTSR.
Keyword:
Super-resolution
Scene text image
Text recognition
Memory learning
Spatial refinement
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
分享收藏MORAN: A Multi-Object Rectified Attention Network for scene text recognitionMORAN: 用于场景文本识别的多目标校正注意力网络.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容