Recent research on super-resolution (SR) has witnessed major developments with the advancements of deep convolutional neural networks. There is a need for information extraction from scenic text images or even document images on device, most of which are low-resolution (LR) images. Therefore, SR becomes an essential pre-processing step as Bicubic Upsampling, which is conventionally present in smartphones, performs poorly on LR images. To give the user more control over his privacy, and to reduce the carbon footprint by reducing the overhead of cloud computing and hours of GPU usage, executing SR models on the edge is a necessity in the recent times. There are various challenges in running and optimizing a model on resource-constrained platforms like smartphones. In this paper, we present a novel deep neural network that reconstructs sharper character edges and thus boosts OCR confidence. The proposed architecture not only achieves significant improvement in PSNR over bicubic upsampling on various benchmark datasets but also runs with an average inference time of 11.7 ms per image. We have outperformed state-of-the-art on the Text330 dataset. We also achieve an OCR accuracy of 75.89% on the ICDAR 2015 TextSR dataset, where ground truth has an accuracy of 78.10%.
翻译:最近对超分辨率(SR)的研究目睹了随着深层进化神经网络的进步而取得的重大进展。 需要从设备上提取精美文本图像甚至文档图像的信息, 其中大部分是低分辨率( LR) 图像。 因此, SR成为了重要的预处理步骤, 因为在智能手机中常见的比比比比比立( Bicubic) 抽取( Bicubic) 抽取( Bicubibic), 在远程图像上表现不佳。 让用户对其隐私有更大的控制, 并通过减少云计算和GPU使用时间的间接和减少碳足迹, 在边缘实施SR模型是近些时候必须的。 在运行和优化像智能手机这样的资源限制的平台模型方面存在各种挑战。 在本文中,我们展示了一个全新的深度神经网络, 重建更清晰的字符边缘, 从而提升了 OCR信心。 拟议的结构不仅使PSNR( PSNR) 大大改进了对各种基准数据集的检查, 而且还以11.7 mms的平均值运行。 我们在2015年的O- CD- Refrial 3 数据中实现了一个2015 的精确。