Adversarial Feature Enhancing Network for EndtoEnd Handwritten Paragraph Recognition To date, offline handwriting paragraph recognition systems either separately crop text line images and recognize them or perform implicit line segmentation by integrating complicated multidimensional long shortterm memory (MDLSTM) with an attention mechanism. The former abovementioned approachs could lead to suboptimal performances while the latter is very timeconsuming. In this paper, a fast endtoend system, called adversarial feature enhancing network (AFEN), is proposed for offline handwritten paragraph recognition. The proposed AFEN system comprises five components: a shared feature extractor for robust feature learning, a text detection branch for text box proposal; RoIRotate for oriented feature region extraction, an adversarial feature learning network for joint feature learning of text detection and recognition branch, and a text recognition branch for text transcription. Experiments on two popular handwritten paragraph recognition benchmarks, namely IAM and Rimes are used to verify the efficacy of the proposed AFEN system. The proposed approach yields impressive results compared to previously proposed systems in the literature.

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.