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

Description: We are Tencent-DPPR (Data Platform Precision Recommendation) team. Our text detector follows the framework of Mask R-CNN that employs mask to detect multi-oriented scene texts. We 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. Our recognition method recognizes text lines and their character-level language types using ensemble results of several recognition models, which based on CTC/Seq2Seq and CNN with self-attention/RNN. Finally, we identify the language types of recognized results based on statics of MLT-2019 and Wikipedia corpus.

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

Description: We are Tencent-DPPR (Data Platform Precision Recommendation) team. Our text detector follows the framework of Mask R-CNN that employs mask to detect multi-oriented scene texts. We 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. Our recognition method recognizes text lines and their character-level language types using ensemble results of several recognition models, which based on CTC/Seq2Seq and CNN with self-attention/RNN. After that, we identify the language types of recognized results based on statics of MLT-2019 and Wikipedia corpus.

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

Description: We are Tencent-DPPR (Data Platform Precision Recommendation) team. Our text detector follows the framework of Mask R-CNN that employs mask to detect multi-oriented scene texts. We 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. Our recognition method uses ensemble results of several recognition models, which based on CTC/Seq2Seq and CNN with self-attention/RNN. Then we identify the language types of recognized results based on statics of MLT-2019 and Wikipedia corpus.