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

    • 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: e2e-mlt + bug fix2019-04-11

    Authors: Yash Patel, Michal Busta, Jiri Matas

    Description: E2E-MLT, an end-to-end trainable unconstrained method for multi-language
    scene text localization and recognition. The method is based on a single fully convolutional network (with shared layers for both tasks.
    The results are plain network outputs - no dictionary has been used.

    Patel, Yash, Michal Bušta, and Jiri Matas. "E2E-MLT-an Unconstrained End-to-End Method for Multi-Language Scene Text." arXiv preprint arXiv:1801.09919 (2018).

    Source code

    method: e2e-mlt2018-10-25

    Authors: Yash Patel, Michal Busta, Jiri Matas

    Description: E2E-MLT, an end-to-end trainable unconstrained method for multi-language
    scene text localization and recognition. The method is based on a single fully convolutional network (with shared layers for both tasks.
    The results are plain network outputs - no dictionary has been used.

    Patel, Yash, Michal Bušta, and Jiri Matas. "E2E-MLT-an Unconstrained End-to-End Method for Multi-Language Scene Text." arXiv preprint arXiv:1801.09919 (2018).

    Source code

    Ranking Table

    Description Paper Source Code
    DateMethodRecallPrecisionHmean
    2018-01-22FOTS69.91%91.09%79.11%
    2019-04-11e2e-mlt + bug fix55.71%58.49%57.07%
    2018-10-25e2e-mlt49.45%55.13%52.13%
    2016-03-02TextProposals + DictNet36.69%76.58%49.61%

    Ranking Graphic