Authors: Dong Mei (梅栋), Xianbiao Qi (齐宪标), Yihao Chen (陈意浩), Di Wu (吴迪), Rong Xiao (肖嵘)
Affiliation: Ping An Property & Casualty Insurance Co of China Ltd. (中国平安财产保险股份有限公司)
Description: Our method ensembles multiple MASTER  and SAR  models. Our training datasets consist of several public datasets, including ReCTS, Art, CTW, RCTW, LSVT, MLT，Baidu Scene Text Recognition contest data (百度中文场景文字识别大赛), and some synthesized data based on CTW and ReCTS.
Our implementation is based on our own toolbox, FastOCR. FastOCR is a simple, fast but powerful text detection and recognition toolbox. We implement CRNN, SAR, MASTER, EAST, PSENet, Mask R-CNN, and several other SOTA methods in FastOCR.
1. Lu, N., Yu, W., Qi, X., Chen, Y., Gong, P., & Xiao, R. (2019). Master: Multi-aspect non-local network for scene text recognition. arXiv preprint arXiv:1910.02562.
2. Li, H., Wang, P., Shen, C., & Zhang, G. (2019, July). Show, attend and read: A simple and strong baseline for irregular text recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 8610-8617).