An EndtoEnd Trainable Framework for Joint Optimization of Document Enhancement and Recognition Recognizing text from degraded and lowresolution document images is still an open challenge in the vision community. Existing text recognition systems require a certain resolution and fails if the document is of lowresolution or heavily degraded or noisy. This paper presents an endtoend trainable deeplearning based framework for joint optimization of document enhancement and recognition. We are using a generative adversarial network (GAN) based framework to perform image denoising followed by deep back projection network (DBPN) for superresolution and use these superresolved features to train a bidirectional long short term memory (BLSTM) with Connectionist Temporal Classification (CTC) for recognition of textual sequences. The entire network is endtoend trainable and we obtain improved results than stateoftheart for both the image enhancement and document recognition tasks. We demonstrate results on both printed and handwritten degraded document datasets to show the generalization capability of our proposed robust framework.

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.