Document Domain Adaptation with Generative Adversarial Networks Despite the digitization of communication flows, information is still commonly exchanged via printed documents. Since manual information extraction becomes inefficient and prone to errors, automatic document processing (ADP) tools have been proposed. Following the current trends in machine learning, many of these tools are based on deep learning methods which require a representative set of training documents to achieve good performance for a target domain. In practice, training documents are often scarce and limited to certain domains which makes it difficult to train a model that generalises well to varying domains. This paper analyses the influence of domain shifts on the performance of document analysis tasks. It further explores the improvements that can be achieved with visual domain adaptation using generative adversarial networks (GANs). The results show that the impact of the domain shift on the performance is not only depending on the difference between the domains but also on the analysis task itself. While some tasks such as document binarization are noticeably affected by the domain shift, other tasks like page classification are less sensitive. It is also shown that the use of mapped training data obtained from a GAN, which translates between the source and target domain, can improve the performance considerably.

Automatic Synthetic Document Image Generation using Generative Adversarial Networks: Application in MobileCaptured Document Analysis In this paper, we propose a method using Generative Adversarial Networks for automatically synthesizing document images that are similar to real printed documents captured by mobile phoneu0027s camera in unconstrained environment. We focus on the simulation of image defects for unconstrained mobile image acquisition procedure (nonuniform illumination, defocusing, optical and mechanical deformations, vibrations, noise in electronic components,...). Our approach is proven to be lowcost as it only requires a collection of real document images without any annotation. Experimental results show the effectiveness of our approach to improve OCR (Optical Character Recognition) recognition rate in a mobilecaptured document images framework. Although in this paper, we focus on modern printed document images, our proposed approach could be extended to another type of documents, including historical one.