Inactive evaluations
method: gts-1000-th_0.42024-03-14
Authors: w
Affiliation: n
Description: 2312
method: TencentAILab2017-10-28
Authors: Jingchao Zhou, Xing Ji, Zheng Zhou, Zhifeng Li
Description: Our method consists of two networks: text detection network employed in task 2.1 and word recognition network employed in task 2.2. Two networks are integrated with cascade training to achieve superior performance.
method: gts-1000-minsize_13002024-03-14
Authors: w
Affiliation: n
Description: 342
Description Paper Source Code
| Date | Method | Recall | Precision | Hmean | |||
|---|---|---|---|---|---|---|---|
| 2024-03-14 | gts-1000-th_0.4 | 94.39% | 99.51% | 96.88% | |||
| 2017-10-28 | TencentAILab | 97.20% | 96.30% | 96.74% | |||
| 2024-03-14 | gts-1000-minsize_1300 | 93.81% | 99.75% | 96.69% | |||
| 2024-03-14 | gts-1000-minsize_1440-th_0.4 | 93.57% | 99.50% | 96.45% | |||
| 2018-01-22 | FOTS | 93.81% | 98.17% | 95.94% | |||
| 2017-06-09 | TextBoxes++ | 92.87% | 98.27% | 95.50% | |||
| 2020-10-30 | MaskTextSpotter | 93.22% | 97.32% | 95.23% | |||
| 2018-03-05 | HoText_v1 | 92.64% | 97.54% | 95.03% | |||
| 2021-02-05 | AIX Lab., LG Electronics | 92.76% | 96.13% | 94.41% | |||
| 2017-03-20 | AdelaideMachineLearning | 91.24% | 97.14% | 94.10% | |||
| 2016-08-23 | HUST_MCLAB | 90.77% | 97.25% | 93.90% | |||
| 2017-07-11 | SRC-B-MachineLearningLab | 91.59% | 95.73% | 93.61% | |||
| 2017-10-28 | MPTSys | 88.20% | 97.42% | 92.58% | |||
| 2016-03-17 | Adelaide_ConvLSTMs | 84.93% | 98.91% | 91.39% | |||
| 2015-04-03 | VGGMaxBBNet_095 | 86.68% | 94.64% | 90.49% | |||
| 2015-04-03 | VGGMaxBBNet (055) | 87.62% | 93.05% | 90.25% | |||
| 2016-03-10 | SRC-B-TextProcessingLab | 84.46% | 95.13% | 89.48% | |||
| 2017-01-19 | Yunos_Robot1.0 | 78.97% | 96.30% | 86.78% | |||
| 2015-04-02 | StradVision-1 | 79.91% | 92.68% | 85.82% | |||
| 2015-10-20 | Deep2Text II+ | 75.82% | 96.29% | 84.84% | |||
| 2015-04-01 | NJU Text (Version3) | 73.25% | 83.16% | 77.89% | |||
| 2015-04-02 | Deep2Text II-2 | 72.08% | 83.49% | 77.37% | |||
| 2015-04-01 | Deep2Text I | 69.74% | 85.78% | 76.93% | |||
| 2015-04-02 | MSER MRF | 66.94% | 87.21% | 75.74% | |||
| 2015-04-02 | Beam search CUNI (decoding for TextSpotter - no postprocessing) | 62.62% | 72.63% | 67.25% | |||
| 2015-05-04 | Baseline OpenCV 3.0 + Tesseract | 52.92% | 77.97% | 63.05% | |||
| 2015-04-02 | Beam search CUNI +S (decoding for TextSpotter - spell checked) | 16.47% | 93.38% | 28.00% | |||
| 2015-04-01 | Baseline-TextSpotter | 0.00% | 0.00% | 0.00% |