method: Tencent-DPPR Team & USTB-PRIR2017-07-01
Authors: Tencent-DPPR Team (Chunchao Guo, Weichen Zhang, Yi Li, Hui Song, Ming Liu, Hongfa Wang, Lei Xiao) & USTB-PRIR (Chun Yang, Zejun Li, Jianwei Wu, Jiebo Hou, Chang Liu, Longhuang Wu, Xu-Cheng Yin)
Description: Tencent-DPPR (Data Platform Precision Recommendation) Team. They detect text regions using improved Rotation Region Proposal Networks. After that they extract features from text lines and employ multiple LSTM-based models to generate different results for each image. Finally, they select the one with the maximum probability among all candidate results. Please refer to the paper entiled "AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition", arXiv, 2017.
method: Foo & Bar2017-06-30
Authors: Zheqi He, Yongtao Wang, Xiang Bai
Description: The method used quadrangle regression network for text detection, and then use homography to transform quadrangle regions to rectangles and finally CRNN (https://github.com/bgshih/crnn) for recognition.
method: WPS2017-06-30
Authors: Hin Lee
Description: A word spotting system that combines a text proposal extractor and a attention-based text recognizer.
| Date | Method | Average Precision | |||
|---|---|---|---|---|---|
| 2017-07-01 | Tencent-DPPR Team & USTB-PRIR | 43.58% | |||
| 2017-06-30 | Foo & Bar | 27.01% | |||
| 2017-06-30 | WPS | 18.82% | |||
| 2017-10-06 | SSD + CRNN (sravya) | 0.86% | |||
| 2017-06-30 | CNN-LSTM based text recognition | 0.73% |