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.

Adversarial Generation of Handwritten Text Images Conditioned on Sequences We propose a system based on Generative Adversarial Networks (GAN) to produce synthetic images of handwritten words. We use bidirectional LSTM recurrent layers to get an embedding of the word to be rendered, and we feed it to the generator network. We also modify the standard GAN by adding an auxiliary network for text recognition. The system is then trained with a balanced combination of an adversarial loss and a CTC loss. Together, these extensions to GAN enable to control the textual content of the generated word images yielding realisticlooking images on both French and Arabic languages. Stateoftheart offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partially satisfy this requirement, we could use those synthetic images to increase the amount of training data. We show that integrating generated images into the existing training data of a text recognition system can slightly enhance its performance.