ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | Proceedings of the International Conference on Document Analysis and Recognition, ICDAR |
Pages (from-to) | 1429-1434 |
Number of pages | 6 |
ISSN | 1520-5363 |
DOIs | |
Publication status | Published - 2 Jul 2017 |
Externally published | Yes |
Event | 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017 - Kyoto, Japan Duration: 9 Nov 2017 → 15 Nov 2017 |
Conference
Conference | 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017 |
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Country | Japan |
City | Kyoto |
Period | 09/11/2017 → 15/11/2017 |
Sponsor | et al., FxPaL, Glory, Hitachi, Media Drive, Sansan |
Bibliographical note
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.
- Competition, Dataset, Detection, Recognition, Text
Research areas
ID: 301826449