Simultaneous Optimisation of Image Quality Improvement and Text Content Extraction from Scanned Documents Convolutional neural networks are shown to achieve breakthrough performance for the task of single image super resolution (SISR) for natural images. These stateoftheart (SOA) networks have been adapted to the task of single text image super resolution and have been shown to boost the optical character recognition (OCR) performance. However, these approaches depend on variations of the standard mean squared error (MSE) loss in order to train the SR network for improving the text image quality which does not guarantee optimal OCR performance. In this paper, we propose to combine the OCR performance into the loss function during network training. This results in the generation of high resolution text images that achieve high OCR performance that is comparable to the ground truth highresolution text images and surpassing those of the SOA baseline results. We define novel intuitive metrics to capture the improvement in the OCR performance and provide extensive experiments to qualitatively and quantitatively assess improvement in the results of our proposed approach against the SOA baselines on the standard UNLV dataset.

Cascaded DetailPreserving Networks for SuperResolution of Document Images 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 lowresolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image superresolution. Our model is composed by the DetailPreserving 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 lowresolution images can upscale gradually by passing through each DetailPreserving Network until the final highresolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent stateoftheart image superresolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results.