Authors: Longhuang Wu, Shangxuan Tian, Chang Liu, Wenjie Cai, Jiachen Li, Chunchao Guo, Sicong Liu, Haoxi Li, Hongfa Wang, Hongkai Chen, Qinglin lu, Xucheng Yin, Lei Xiao

Description: We are Tencent-DPPR (Data Platform Precision Recommendation) team. In detection stage, we use LSVT dataset to pretrain our model and provided ReCTS dataset to train the text detector. During training, we use multi-scale training policy.
Our text detector is based on two-stage method. In backbone part, we use ResNet101 as feature extractor. In FPN part, we designed a policy to help proposals select feature pyramid layers to extract features instead of choosing one layer according to box sizes.
In detection ensemble part, we apply a multi-scale test method with different backones. When ensembling all the results, we develop an approach to vote boxes after scoring each box.