method: BreSee OCR2021-07-06

Authors: Tianpeng Li, Linzhi Zhuang, Mengyue Shao, Jie Wu, Jiling Wu

Affiliation: BreSee AI Lab, Zhejiang Sci-Tech University

Description: We modify the SRN network structure and loss function, synthesize a large number of data sets, train SRN and fine tune on the training set, and use a large number of data enhancement operations.

D. Yu et al., "Towards Accurate Scene Text Recognition With Semantic Reasoning Networks," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 12110-12119, doi: 10.1109/CVPR42600.2020.01213.

method: H&H Lab2019-04-22

Authors: HUST_VLRGROUP(Hui Zhang, Mingkun Yang, Mengde Xu, Zhen Zhu, Jiehua Yang) & HUAWEI_CLOUD_EI(Jing Wang, Yibin Ye, Shenggao Zhu, Dandan Tu)

Description: We mainly completed the task 2 using CRNN. Different from the content in the paper, we modified the structure of CNN to PVANet-like and used multiple GRU layers. And we adjusted the training strategy to continue to improve the recognition result.

[1] Shi, Baoguang, Xiang Bai, and Cong Yao. "An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition." IEEE transactions on pattern analysis and machine intelligence 39.11 (2017): 2298-2304.

method: Hanoi University of Science and Technology - MSOLab2020-05-15

Authors: Nguyen Hoang Thuan

Email: thuan.nh173393@sis.hust.edu.vn

Description: we use CRNN-CTC as baseline

Improving CRNN with EfficientNet-like feature extractor and multi-head attention for text recognition

Ranking Table

Description Paper Source Code
DateMethodRecallPrecisionHmean
2021-07-06BreSee OCR96.78%96.92%96.85%
2019-04-22H&H Lab96.35%96.52%96.43%
2020-05-15Hanoi University of Science and Technology - MSOLab94.77%94.88%94.82%
2019-04-22CLOVA OCR94.30%94.88%94.59%
2019-04-23BOE_IOT_AIBD87.84%86.66%87.24%
2019-04-22BiLstmCtcIgnoreSpaces-Segment83.38%87.37%85.33%
2019-04-22IFLYTEK-textRec_v480.63%81.72%81.17%
2021-04-14Practicing project for Scientific Research Subject (HCMUS master program)63.45%67.44%65.39%
2019-04-18CTPN_CRNN35.75%63.89%45.85%
2019-04-22A Text Extraction Method Based on Modified CRNN26.33%72.53%38.63%
2019-04-18BiLSTM+ctc28.75%49.69%36.42%
2019-04-23CRNN_Pytorch_BiCTC21.43%45.65%29.16%
2019-04-16VIL0.00%0.00%0.00%

Ranking Graphic