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%.
Text Region Extraction from Business Card Images for Mobile Devices
School of Mobile Computing and Communication, Jadavpur University（贾达沃普尔大学移动计算与通信学院）；Department of Computer Science & Engineering, Jadavpur University（贾达沃普尔大学计算机科学与工程系）
MobileVidFactory: Automatic Diffusion-Based Social Media Video Generation for Mobile Devices from Text
University of Electronic Science and Technology of China（电子科技大学）；Microsoft Research（微软研究院）；University of Rochester（罗切斯特大学）；National Taiwan University（国立台湾大学）
DEVICE: Depth and Visual Concepts Aware Transformer for OCR-based Image Captioning
School of Artificial Intelligence at Guangxi University（广西大学人工智能学院）；College of General Education, Guangxi Arts University（广西艺术学院文学院）；The State Key Laboratory of Media Convergence and Communication, Communication University of China（中国传媒大学媒体融合与传播国家重点实验室）；Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment（广西智能控制与电力设备关键实验室）；School of Software Engineering, South China University of Technology（华南理工大学软件学院）
Design of an Optical Character Recognition System for Camera-based Handheld Devices
School of Mobile Computing and Communication, Jadavpur University（贾达普大学移动计算与通信学院）；MCKV Institute of Engineering（MCKV工程学院）；Department of Computer Science and Engineering, Jadavpur University（贾达普大学计算机科学与工程系）
