method: Tencent TEG OCR2020-03-15
Authors: Pei Xu, Shan Huang, Shen Huang, Qi Ju.
Description: We reimplemented the standalone recognition method according to the end-to-end text spotting code released by the Mask TextSpotter[TPAMI]. It is a seq-to-seq method based on 2D attention. We synthesize curved text images for pretraining by the method of VGG synthtext. We add public dataset including icdar2013-2015, CUTE, SVT, IIIT5k, RCTW2017, LSVT,ctw to finetune and don't use any private data.
method: CRAFT (Preprocessing) + TPS-ResNet2019-05-01
Authors: Jeonghun Baek, Moonbin Yim, Junyeop Lee, and Hwalsuk Lee
Description: Before text recognition, we used the text detector called CRAFT as a preprocessing step.
For a recognition model, 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.
method: TPS-ResNet2019-04-30
Authors: Jeonghun Baek, Moonbin Yim, Junyeop Lee, 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.
| Date | Method | Result | Total words | Correct words | |||
|---|---|---|---|---|---|---|---|
| 2020-03-15 | Tencent TEG OCR | 85.74% | 48426 | 35011 | |||
| 2019-05-01 | CRAFT (Preprocessing) + TPS-ResNet | 85.32% | 48426 | 34206 | |||
| 2019-04-30 | TPS-ResNet | 83.63% | 48426 | 33173 | |||
| 2019-04-30 | PKU Team Zero | 65.06% | 48426 | 26216 | |||
| 2019-04-29 | NPU-ASGO | 63.82% | 48426 | 25341 | |||
| 2019-04-21 | Arbitrary shape scene text recognition based on CNN and Attention Enhanced Bi-directional LSTM | 54.49% | 48426 | 19792 |