On-Device Text Image Super Resolution
Dhruval Jain, Arun D Prabhu, Gopi Ramena, Manoj Goyal, Debi Prasanna, Mohanty, Sukumar Moharana, Naresh Purre

TL;DR
This paper introduces a novel deep neural network for on-device text image super-resolution that improves OCR accuracy and runs efficiently on resource-limited devices like smartphones, reducing reliance on cloud processing.
Contribution
The paper presents a new neural network architecture optimized for edge devices, enhancing image sharpness and OCR performance while maintaining fast inference times.
Findings
Achieves higher PSNR than bicubic upsampling on benchmark datasets.
Runs with an average inference time of 11.7 ms per image.
Attains 75.89% OCR accuracy on ICDAR 2015 TextSR dataset.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
