Super-resolution reconstruction is aimed at generating images of high spatial resolution from low-resolution observations. State-of-the-art super-resolution techniques underpinned with deep learning allow for obtaining results of outstanding visual quality, but it is seldom verified whether they constitute a valuable source for specific computer vision applications. In this paper, we investigate the possibility of employing super-resolution as a preprocessing step to improve optical character recognition from document scans. To achieve that, we propose to train deep networks for single-image super-resolution in a task-driven way to make them better adapted for the purpose of text detection. As problems limited to a specific task are heavily ill-posed, we introduce a multi-task loss function that embraces components related with text detection coupled with those guided by image similarity. The obtained results reported in this paper are encouraging and they constitute an important step towards real-world super-resolution of document images.

翻译：超分辨率重建旨在从低分辨率观测中生成高空间分辨率图像。基于深度学习的最先进超分辨率技术能够获得卓越的视觉质量结果，但很少验证其是否为特定计算机视觉应用提供有价值的输入源。本文研究了将超分辨率作为预处理步骤以提升文档扫描光学字符识别性能的可能性。为此，我们提出以任务驱动方式训练单图像超分辨率深度网络，使其更适应文本检测需求。由于特定任务问题具有严重的不适定性，我们引入了一种多任务损失函数，该函数融合了与文本检测相关的组件以及由图像相似性引导的组件。本文报告的实验结果令人鼓舞，为文档图像的真实场景超分辨率重建迈出了重要一步。