Cascaded Detail-Preserving Networks for Super-Resolution of Document Images
This work addresses a domain-specific problem for document image processing by providing an incremental improvement in super-resolution to enhance OCR accuracy.
The paper tackles the problem of low-resolution document images hampering OCR accuracy by proposing cascaded Detail-Preserving Networks for super-resolution, demonstrating that it outperforms state-of-the-art methods and significantly improves OCR recognition results.
The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the low-resolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image super-resolution. Our model is composed by the Detail-Preserving Networks with small magnification. The loss function with perceptual terms is designed to simultaneously preserve the original patterns and enhance the edge of the characters. These networks are trained with the same architecture and different parameters and then assembled into a pipeline model with a larger magnification. The low-resolution images can upscale gradually by passing through each Detail-Preserving Network until the final high-resolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent state-of-the-art image super-resolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results.