Improving Text Image Resolution using a Deep Generative Adversarial Network for Optical Character Recognition Optical character recognition (OCR) has been widely studied in previous work. Except for the models used, the recognition accuracy depends most on the resolution of the image to be recognized. To enhance OCR performance, this paper proposes an approach based on a generative adversarial network to improve text image resolution. Our approach uses a perceptual loss function that consists of an adversarial loss, a content loss and an L1 loss. The adversarial loss and the L1 loss are used to ensure the generated superresolved images are closer to the ground truth highresolution images. Meanwhile, the content loss is used to ensure the generated superresolved images and the input lowresolution images have similar features on the basis of perceptual instead of pixel similarity. To evaluate the proposed approach, we compare the recognition accuracies before and after improving the resolution of both English and Chinese text images. The results show that the recognition accuracies on the superresolved text images obtained with our approach are significantly higher than those on the lowresolution images without processing.

Data Augmentation via Adversarial Networks for Optical Character RecognitionConference Submissions With the ongoing digitalization of ressources across the industry, robust OCR solutions (Optical Character Recognition) are highly valuable. In this work, we aim at designing models to read typical damaged faxes and PDF files and training them with unlabeled data. Stateofart deep learning architectures require scalable tagged datasets that are often difficult and costly to collect. To ensure compliance standards or to provide reproducible cheap and fast solutions for training OCR systems, producing datasets that mimic the quality of the data that will be passed to the model is paramount. In this paper we discuss using unsupervised imagetoimage translation methods to learn transformations that aim to map clean images of words to damaged images of words. The quality of the transformation is evaluated through the OCR brick and these results are compared to the Inception Score (IS) of the GANs we used. That way we are able to generate an arbitrary large realistic dataset without labeling a single observation. As a result, we propose an endtoend OCR training solution to provide competitive models.