• method: CPN (multi-scale)2024-05-30

    Authors: Longhuang Wu, Jianqiang Liu, Hanfei Xu, Bin Zheng, Youxin Wang, Shangxuan Tian, Pengfei Xiong

    Email: wlonghuang@gmail.com

    Description: Detector adopts CPN and recognizer adopts PARSeq.

    Wu L, Tian S, Wang Y, et al. CPN: Complementary Proposal Network for Unconstrained Text Detection[J]. arXiv preprint arXiv:2402.11540, 2024.

    Bautista D, Atienza R. Scene text recognition with permuted autoregressive sequence models[C]//European conference on computer vision. Cham: Springer Nature Switzerland, 2022: 178-196.

    method: TH-DL2022-02-07

    Authors: Ruijie Yan, Shanyu Xiao, Liangrui Peng, Gang Yao, Haodong Shi, Pei Tang, Ning Ding

    Affiliation: Tsinghua University

    Email: yrj17@mails.tsinghua.edu.cn

    Description: For detection, we use an improved Mask-RCNN model. For recognition, we use a PREN2D model that consists of a primitive representation learning network (PREN) and a modified Transformer. The modified Transformer exploits contextual information, and PREN further provides global visual guidance for the decoding process to achieve better performance.

    Yan R., Peng L., Xiao S., et al. Primitive representation learning for scene text recognition. CVPR 2021: 284-293.

    method: Baidu-VIS2020-06-30

    Authors: VIS-VAR Team, Baidu Inc.*

    Affiliation: VIS-VAR Team, Baidu Inc.*

    Description: We are from the Department of Computer Vison, Baidu Inc. Our method mainly composes of three parts:Text detection, Script identification and Text recognition. Text detection mainly relies on LOMO and EAST, Multi-scale testing is adopted and the final result is boosted with Resnet-50 and Inception-v4 as different backbones. Next, all text lines are recognized by the unified language classification model to identify the script of the text. Eight single-language text recognition models based on Res-SENet are used to finally recognize the text line images.

    Ranking Table

    Description Paper Source Code
    DateMethodHmeanPrecisionRecallAverage Precision1-NED1-NED (Case Sens.)Hmean (Case Sens.)
    2024-05-30CPN (multi-scale)63.78%67.07%60.79%48.87%67.92%67.26%62.64%
    2022-02-07TH-DL61.76%74.16%52.91%45.58%58.76%56.88%59.15%
    2020-06-30Baidu-VIS59.72%72.82%50.62%41.32%57.26%56.97%59.01%
    2019-06-04Tencent-DPPR Team & USTB-PRIR59.15%71.26%50.55%35.92%58.46%58.10%58.37%
    2019-06-03Tencent-DPPR Team & USTB-PRIR (Method_v0.2)58.92%71.67%50.02%41.76%58.00%57.64%58.14%
    2019-06-03end2end52.50%55.34%49.93%40.89%58.47%57.85%51.61%
    2019-06-03CRAFTS51.74%65.68%42.68%34.95%48.27%47.75%50.74%
    2019-05-27Tencent-DPPR Team & USTB-PRIR (Method_v0.1)51.70%56.12%47.93%26.88%56.18%55.65%50.86%
    2023-05-22DeepSolo++ (ResNet-50)51.22%62.31%43.49%35.86%52.95%52.61%50.52%
    2019-06-04mask_rcnn-transformer51.04%52.51%49.64%25.96%55.71%54.10%49.34%
    2019-06-03mask_rcnn-transformer50.44%51.90%49.07%25.34%55.28%54.14%49.11%
    2023-08-07spotter47.83%67.46%37.05%29.07%43.74%43.31%46.88%
    2019-05-28CRAFTS(Initial)46.99%66.21%36.41%30.54%42.52%42.01%45.97%
    2019-06-04Three-stage method40.19%44.37%36.73%17.82%46.01%43.86%37.45%
    2019-06-03baseline39.55%39.71%39.39%15.54%43.30%40.18%36.58%
    2019-06-03icdar2019_mlt_test_lqj38.75%39.88%37.67%14.87%49.89%48.95%37.51%
    2019-06-04TH-DL-v237.32%41.22%34.10%19.73%46.19%45.68%36.50%
    2019-06-03TH-DL-v134.49%38.10%31.51%17.48%42.76%42.25%33.69%
    2019-06-04RRPN+CLTDR33.82%38.62%30.08%11.57%38.34%37.90%33.09%
    2019-06-03NXB OCR32.07%34.37%30.06%10.35%35.48%35.06%31.50%
    2019-05-27TH-DL31.69%35.13%28.87%14.33%40.39%39.82%30.79%
    2019-05-27NXB OCR28.42%33.39%24.74%7.96%31.50%31.19%27.93%
    2019-05-22E2E-MLT26.46%37.44%20.47%7.72%26.39%25.71%24.85%
    2019-05-24First submission0.00%0.00%0.00%0.00%0.00%0.00%0.00%
    2019-05-27dummy0.00%0.00%0.00%0.00%0.00%0.00%0.00%

    Ranking Graphic

    Ranking Graphic