• 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: 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.

    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

    Ranking Table

    Description Paper Source Code
    DateMethodRecallPrecisionHmean
    2021-02-05AIX Lab., LG Electronics 88.88%88.59%88.73%
    2017-07-11SRC-B-MachineLearningLab84.19%87.43%85.78%
    2018-01-22FOTS80.70%89.26%84.77%
    2016-08-23HUST_MCLAB79.50%88.79%83.89%
    2016-03-17Adelaide_ConvLSTMs72.52%89.50%80.12%
    2016-03-10SRC-B-TextProcessingLab74.92%81.59%78.11%
    2015-10-20Deep2Text II+69.68%86.00%76.99%
    2015-04-02Deep2Text II-269.79%81.74%75.29%
    2015-04-01Deep2Text I66.74%83.95%74.36%
    2015-04-02StradVision-164.99%69.46%67.15%
    2015-04-02Beam search CUNI (decoding for TextSpotter - no postprocessing)52.89%59.66%56.07%
    2015-04-02Beam search CUNI +S (decoding for TextSpotter - spell checked)11.89%65.27%20.11%
    2015-04-01Baseline-TextSpotter0.00%0.00%0.00%

    Ranking Graphic