method: BASELINE v12019-05-01

Authors: USTC-iFLYTEK

Description: We presented a simple baseline based on traditional image classification methods and its ensemble. Besides, some data augmentation tricks are used during training, e.g., randomly crop/rotate/scale, synthetic characters mix-up. We didn't use any external data.
name organization
Xiangxiang Wang(王翔翔) iFLYTEK(科大讯飞)
Shuai Shao(邵帅) iFLYTEK(科大讯飞)
Hao Wu(吴浩) iFLYTEK(科大讯飞)
Chenyu Liu(刘辰宇) iFLYTEK(科大讯飞)
Yixing Zhu(朱意星) USTC(中国科技大学)
Zhengyan Yang(杨争艳) iFLYTEK(科大讯飞)
Changjie Wu(吴昌杰) USTC(中国科技大学)
Mobai Xue(薛莫白) USTC(中国科技大学)
Jiajia Wu(吴嘉嘉) iFLYTEK(科大讯飞)
Bing Yin(殷兵) iFLYTEK(科大讯飞)
Cong Liu(刘聪) iFLYTEK(科大讯飞)
Jinshui Hu(胡金水) iFLYTEK(科大讯飞)
Jun Du(杜俊) USTC(中国科技大学)
Jianshu Zhang(张建树) USTC(中国科技大学)
Lirong Dai(戴礼荣) USTC(中国科技大学)

method: Amap_CVLab2019-04-30

Authors: Tenghui Wang, Xinran Liu, Zhihui Hao

Description: modify the res-block sequence details

method: TPS-ResNet v12019-04-30

Authors: Jeonghun Baek, Moonbin Yim, Sungrae Park, and Hwalsuk Lee

Description: We used Thin-plate-spline (TPS) based Spatial transformer network (STN) which normalizes the input text images, ResNet based feature extractor, BiLSTM, and attention mechanism.
This model was developed based on the analysis of scene text recognition modules.
See our paper and source code.

[Training Data]
At first, we generated the Chinese synthetic datasets by MJSynth and SynthText code, then pre-trained our model with the synthetic dataset and real dataset (ArT, LSVT, ReCTS, and RCTW). After that, we finetuned it with ReCTS data.

Ranking Table

Description Paper Source Code
DateMethodResult
2019-05-01BASELINE v197.37%
2019-04-30Amap_CVLab97.27%
2019-04-30TPS-ResNet v196.11%
2019-04-30SANHL_v495.94%
2019-04-221295.36%
2019-04-29Tencent-DPPR Team95.12%
2019-04-2312395.00%
2019-04-30ResNet_HUSTer94.73%
2019-04-29ResNet_HUST94.54%
2019-04-30ReCTS_Task193.89%
2019-04-30Task1-re593.87%
2019-09-06cool and cool93.55%
2019-04-30ocr_densenet93.47%
2019-04-30MixNet based on multiple classic CNN93.19%
2019-04-29class_5435_scale_292.95%
2019-04-23Task 1 - Character Recognition92.81%
2019-04-23task1_19042391.04%
2019-04-30task1_389.91%
2019-04-27Siamese Net89.79%
2019-04-26Subm190426_ensemble0189.14%
2019-04-29task1_0488.82%
2019-04-26casual train train87.32%
2019-04-30LCT_OCR (中国科学院信息工程研究所)8.86%
2019-04-28jxl_ocr6.68%

Ranking Graphic