• method: TH-DL (fine-tuned)2025-03-20

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

    Affiliation: Tsinghua University

    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. The recognition model is fine-tuned on the RRC-MLT-2019 training set for 10 epochs.

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

    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.

    Ranking Table

    Description Paper Source Code
    DateMethodHmeanPrecisionRecallAverage Precision1-NED1-NED (Case Sens.)Hmean (Case Sens.)
    2025-03-20TH-DL (fine-tuned)65.15%75.47%57.31%50.29%62.67%62.26%64.22%
    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