Update README.md

Updated owner

research/README.md

@@ -26,7 +26,7 @@ The research models are maintained by their respective authors.
 | [adversarial_crypto](adversarial_crypto) | [Learning to Protect Communications with Adversarial Neural Cryptography](https://arxiv.org/abs/1610.06918) | Code to train encoder/decoder/adversary network triplets and evaluate their effectiveness on randomly generated input and key pairs | dave-andersen |
 | [adversarial<br />_logit_pairing](adversarial_logit_pairing)   | [Adversarial Logit Pairing](https://arxiv.org/abs/1803.06373) | Implementation of Adversarial logit pairing paper as well as few models pre-trained on ImageNet and Tiny ImageNet   | alexeykurakin |
 | [adversarial_text](adversarial_text) | [1] [Adversarial Training Methods for Semi-Supervised Text](https://arxiv.org/abs/1605.07725) Classification<br/>[2] [Semi-supervised Sequence Learning](https://arxiv.org/abs/1511.01432) | Adversarial Training Methods for Semi-Supervised Text Classification| rsepassi, a-dai |
-| [attention_ocr](attention_ocr)   | [Attention-based Extraction of Structured Information from Street View Imagery](https://arxiv.org/abs/1704.03549) | | alexgorban |
+| [attention_ocr](attention_ocr)   | [Attention-based Extraction of Structured Information from Street View Imagery](https://arxiv.org/abs/1704.03549) | | xavigibert |
 | [audioset](audioset) | Models for AudioSet: A Large Scale Dataset of Audio Events | | plakal, dpwe |
 | [autoaugment](autoaugment) | [1] [AutoAugment](https://arxiv.org/abs/1805.09501)<br/>[2] [Wide Residual Networks](https://arxiv.org/abs/1605.07146)<br/>[3] [Shake-Shake regularization](https://arxiv.org/abs/1705.07485)<br/>[4] [ShakeDrop Regularization for Deep Residual Learning](https://arxiv.org/abs/1802.02375) | Train Wide-ResNet, Shake-Shake and ShakeDrop models on CIFAR-10 and CIFAR-100 dataset with AutoAugment | barretzoph |
 | [autoencoder](autoencoder) | Various autoencoders | | snurkabill |