On-Device Text Image Super Resolution
| Autor: | Manoj Goyal, Gopi Ramena, Naresh Purre, Debi Prasanna Mohanty, Sukumar Moharana, Arun D Prabhu, Dhruval Jain |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Ground truth Artificial neural network Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION computer.software_genre Convolutional neural network Upsampling Information extraction Benchmark (computing) Bicubic interpolation Overhead (computing) Computer vision Artificial intelligence business computer |
| Zdroj: | ICPR |
| Popis: | 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%. Accepted to the International Conference on Pattern Recognition(ICPR), 2020 |
| Databáze: | OpenAIRE |
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