ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
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ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17). / Shi, Baoguang; Yao, Cong; Liao, Minghui; Yang, Mingkun; Xu, Pei; Cui, Linyan; Belongie, Serge; Lu, Shijian; Bai, Xiang.
In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 02.07.2017, p. 1429-1434.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
AU - Shi, Baoguang
AU - Yao, Cong
AU - Liao, Minghui
AU - Yang, Mingkun
AU - Xu, Pei
AU - Cui, Linyan
AU - Belongie, Serge
AU - Lu, Shijian
AU - Bai, Xiang
N1 - Funding Information: ACKNOWLEDGMENT The challenge is supported in part by NSFC 61222308. The authors thank Dr. Fei Yin and Dr. Cheng-Lin Liu for their suggestions. The authors also thank Zhiyong Liu, Yang Yang, Zhiqiang Zhang, Rui Yu and Xuelei Zhang for their efforts in annotating the data. Publisher Copyright: © 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Chinese is the most widely used language in the world. Algorithms that read Chinese text in natural images facilitate applications of various kinds. Despite the large potential value, datasets and competitions in the past primarily focus on English, which bares very different characteristics than Chinese. This report introduces RCTW, a new competition that focuses on Chinese text reading. The competition features a large-scale dataset with over 12,000 annotated images. Two tasks, namely text localization and end-To-end recognition, are set up. The competition took place from January 20 to May 31, 2017. 23 valid submissions were received from 19 teams. This report includes dataset description, task definitions, evaluation protocols, and results summaries and analysis. Through this competition, we call for more future research on the Chinese text reading problem.
AB - Chinese is the most widely used language in the world. Algorithms that read Chinese text in natural images facilitate applications of various kinds. Despite the large potential value, datasets and competitions in the past primarily focus on English, which bares very different characteristics than Chinese. This report introduces RCTW, a new competition that focuses on Chinese text reading. The competition features a large-scale dataset with over 12,000 annotated images. Two tasks, namely text localization and end-To-end recognition, are set up. The competition took place from January 20 to May 31, 2017. 23 valid submissions were received from 19 teams. This report includes dataset description, task definitions, evaluation protocols, and results summaries and analysis. Through this competition, we call for more future research on the Chinese text reading problem.
KW - Competition
KW - Dataset
KW - Detection
KW - Recognition
KW - Text
UR - http://www.scopus.com/inward/record.url?scp=85045202391&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2017.233
DO - 10.1109/ICDAR.2017.233
M3 - Conference article
AN - SCOPUS:85045202391
SP - 1429
EP - 1434
JO - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
JF - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SN - 1520-5363
T2 - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
Y2 - 9 November 2017 through 15 November 2017
ER -
ID: 301826449