Authors: HUST_VLRGROUP(Hui Zhang, Mingkun Yang, Mengde Xu, Zhen Zhu, Jiehua Yang) & HUAWEI_CLOUD_EI(Jing Wang, Yibin Ye, Shenggao Zhu, Dandan Tu)

Description: We mainly completed the task 2 using CRNN. Different from the content in the paper, we modified the structure of CNN to PVANet-like and used multiple GRU layers. And we adjusted the training strategy to continue to improve the recognition result.

Authors: Sungrae Park, Seung Shin, Seonghyeon Kim, Jaeheung Surh, Junyeop Lee, Hwalsuk Lee

Description: Our model consists of a ResNet-based backbone, a sequence model, and an attention-based decoder [1]. The backbone is a combination of the ResNet and SENet(squeeze and excitation network) [2] and the others are based on Baek et al. [1]. We trained the model with our own synthetic datasets by applying virtual adversarial training (VAT) techniques [3]. For this competition, we fine-tuned the model with the training dataset of SROIE. The recognition identified the texts on the detected text boxes by CRAFTS [4].