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

Description: We are Tencent-DPPR (Data Platform Precision Recommendation) team. Our method follows the framework of Mask R-CNN that employs mask to detect multi-oriented scene texts. We use the MLT-19 and the MSRA-TD500 dataset to train our text detector, and we also apply a multi-scale training approach during training. To obtain the final ensemble results, we combined two different backbones and different multi-scale testing approaches.

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

Description: We are Tencent-DPPR (Data Platform Precision Recommendation) team. Our method follows the framework of Mask R-CNN that employs mask to detect multi-oriented scene texts. We use the MLT-19 and the MSRA-TD500 dataset to train our text detector, and we also apply a multi-scale training approach during training. To obtain the final ensemble results, we combined two different backbones and different multi-scale testing approaches.

Authors: Pengfei Wang~*, Mengyi En*, Xiaoqiang Zhang*, Chengquan Zhang*

Affiliation: VIS-VAR Team, Baidu Inc.*; Xidian University~

Description: The method mainly relies on a two-stage text detector, namely LOMO [1], which is inspired by Mask-R-CNN and where an iterative refinement module is introduced to refine the boundary of text region once or more times during testing to get the more accurate detection results. As extra data sets, ICDAR15 and partial KAIST are also used in the training phase. Multi-scale testing is adopted and the final result is boosted from LOMOs with Resnet-50 and Inception-v4 as different backbones.

*This work is done when Pengfei Wang is an intern at Baidu Inc.