Inactive evaluations
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: FOTS2018-01-22
Authors: Xuebo Liu, Ding Liang, Shi Yan, Dagui Chen, Yu Qiao, Junjie Yan
Description: A unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks.
method: AIX Lab., LG Electronics 2021-02-05
Authors: Wonju Lee and Joseph Lim
Affiliation: AI Lab., Future Tech. Center, CTO, LG Electronics
Email: wonju2.lee@lge.com
Description: We reimplemented the end-to-end text spotting method combined the Mask TextSpotter with Deformable ConvNets. We tried to pre-train synthText images for character-level annotations and to fine-tune real world images like following papers for the generalization. We don't use any private data.
Date | Method | Recall | Precision | Hmean | |||
---|---|---|---|---|---|---|---|
2017-10-28 | TencentAILab | 97.20% | 96.30% | 96.74% | |||
2018-01-22 | FOTS | 93.81% | 98.17% | 95.94% | |||
2021-02-05 | AIX Lab., LG Electronics | 92.76% | 96.13% | 94.41% | |||
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% | |||
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% | |||
2015-04-02 | 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% |