• 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

    Ranking Table

    Description Paper Source Code
    DateMethodRecallPrecisionHmean
    2021-02-05AIX Lab., LG Electronics 93.02%97.93%95.41%
    2018-01-22FOTS88.33%95.97%91.99%

    Ranking Graphic