- Task 1 - Text Localization
- Task 2 - Script identification
- Task 3 - Joint text detection and script identification
- Task 4 - End-to-End text detection and recognition
method: Tencent-DPPR Team2019-06-04
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.
method: Tencent-DPPR Team2019-06-03
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.
method: Tencent-DPPR Team (Method_v0.1)2019-05-27
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.
Date | Method | Hmean | Precision | Recall | Average Precision | |||
---|---|---|---|---|---|---|---|---|
2019-06-04 | Tencent-DPPR Team | 80.84% | 87.68% | 74.99% | 71.72% | |||
2019-06-03 | Tencent-DPPR Team | 80.40% | 88.46% | 73.69% | 70.45% | |||
2019-05-27 | Tencent-DPPR Team (Method_v0.1) | 75.64% | 82.10% | 70.11% | 57.66% | |||
2019-05-27 | Tencent-DPPR Team (Method_v0.2) | 75.64% | 82.10% | 70.11% | 57.66% | |||
2019-06-04 | mask_rcnn-transformer | 75.12% | 77.26% | 73.10% | 56.31% | |||
2019-06-03 | mask_rcnn-transformer | 74.62% | 76.74% | 72.61% | 55.52% | |||
2019-06-04 | TH-DL-v2 | 71.01% | 78.34% | 64.94% | 57.21% | |||
2019-06-03 | TH-DL-v1 | 70.19% | 77.44% | 64.17% | 56.38% | |||
2019-05-27 | TH-DL | 69.65% | 77.08% | 63.52% | 55.34% | |||
2019-05-29 | DISTILLED CRAFT | 68.69% | 74.97% | 63.39% | 54.82% | |||
2019-06-03 | CRAFTS | 68.34% | 78.52% | 60.50% | 53.75% | |||
2019-06-03 | sot + classifier | 65.66% | 66.20% | 65.13% | 59.49% | |||
2019-05-28 | CRAFTS(Initial) | 62.23% | 72.66% | 54.43% | 48.57% | |||
2019-06-03 | NXB OCR | 57.74% | 61.79% | 54.18% | 33.55% | |||
2019-05-27 | NXB OCR | 54.51% | 63.87% | 47.55% | 30.44% | |||
2019-05-27 | TDSI-SE | 3.86% | 4.44% | 3.41% | 0.15% |