• File
  • Strongly Contextualised results
  • Weakly Contextualised results
  • Generic results
  • Evaluation
  • End-to-End recognition
  • Word Spotting

    • 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.

    Mask TextSpotter: An End-to-End TrainableNeural Network for Spotting Text with Arbitrary Shapes Minghui Liao, Pengyuan Lyu, Minghang He, Cong Yao, Wenhao Wu, Xiang Bai

    Deformable ConvNets v2: More Deformable, Better Results Xizhou Zhu, Han Hu, Stephen Lin, Jifeng Dai

    Source code

    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.

    FOTS: Fast Oriented Text Spotting with a Unified Network, accepted by CVPR 2018

    method: SRC-B-MachineLearningLab2017-07-11

    Authors: Shuli Yang, Yingying Jiang, Xiaobing Wang, Xiangyu Zhu, Pei Fu, Hua Wang, Zhenbo Luo

    Description: Samsung R&D Institute of China - Beijing. Machine Learning Lab. Our method is based on 2.1 and 2.3 (updated version). The paper is in preparation.

    Ranking Table

    Description Paper Source Code
    DateMethodRecallPrecisionHmean
    2021-02-05AIX Lab., LG Electronics 88.32%88.63%88.47%
    2018-01-22FOTS85.05%90.66%87.76%
    2017-07-11SRC-B-MachineLearningLab86.57%88.64%87.59%
    2016-08-23HUST_MCLAB82.01%90.23%85.92%
    2016-03-17Adelaide_ConvLSTMs75.93%91.29%82.91%
    2016-03-10SRC-B-TextProcessingLab78.50%84.32%81.31%
    2015-10-20Deep2Text II+72.08%87.15%78.90%
    2015-04-02Deep2Text II-272.08%83.49%77.37%
    2015-04-01Deep2Text I69.74%85.78%76.93%
    2015-04-02StradVision-168.34%72.13%70.19%
    2015-04-02Beam search CUNI (decoding for TextSpotter - no postprocessing)56.19%63.04%59.42%
    2015-04-02Beam search CUNI +S (decoding for TextSpotter - spell checked)12.97%67.68%21.76%
    2015-04-01Baseline-TextSpotter0.00%0.00%0.00%

    Ranking Graphic