- Task 1 - Text Localization
- Task 2 - Script identification
- Task 3 - Joint text detection and script identification
method: 4Paradigm-Data-Intelligence2019-06-03
Authors: Feng Cheng, Lixin Gu, Qingjie Liu, Feng Han, Jingtao Han
Description: The detection model and recognition model are trained separately.
Detection model: Based on Mask-RCNN. multi-scale. Train-set: 2017 MLT task1 train-set.
Recognition model: Based on Transformer with backbone ResNet50. A voting process is done to identify the language of recognized transcript. Train-set: 2017 MLT task2 train-set & 2019 MLT task2 train-set & 2019 MLT Synthetic dataset.
method: DeepSolo++ (ResNet-50)2023-05-22
Authors: Maoyuan Ye
Description: DeepSolo++ (Res-50, routing, #3)
@inproceedings{ye2022deepsolo, title={DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting}, author={Ye, Maoyuan and Zhang, Jing and Zhao, Shanshan and Liu, Juhua and Liu, Tongliang and Du, Bo and Tao, Dacheng}, booktitle={CVPR}, year={2023} }
method: PMTD + CNN based method2019-09-28
Authors: Geonho Hwang
Affiliation: NCIA, Seoul National University
Description: Task1: PMTD
Task2: CNN based method
| Date | Method | Hmean | Precision | Recall | Average Precision | |||
|---|---|---|---|---|---|---|---|---|
| 2019-06-03 | 4Paradigm-Data-Intelligence | 75.23% | 79.26% | 71.60% | 56.65% | |||
| 2023-05-22 | DeepSolo++ (ResNet-50) | 74.11% | 84.61% | 65.93% | 63.34% | |||
| 2019-09-28 | PMTD + CNN based method | 72.64% | 78.76% | 67.39% | 61.48% | |||
| 2019-06-04 | CLOVA-AI | 68.31% | 74.52% | 63.06% | 54.56% | |||
| 2019-05-10 | Ashwaq | 58.11% | 62.44% | 54.34% | 40.61% | |||
| 2017-06-30 | SCUT-DLVClab2 | 58.08% | 71.78% | 48.77% | 41.42% | |||
| 2017-06-30 | TH-DL | 39.37% | 58.58% | 29.65% | 24.54% |