ICDAR2019基于任意形状文本的鲁棒阅读挑战- RRC-ArT

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI:10.1109/ICDAR.2019.00252

Chee-Kheng Chng, Yuliang Liu, Yipeng Sun, Chun Chet Ng, Canjie Luo, Zihan Ni, Chuanming Fang, Shuaitao Zhang, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin

{"title":"ICDAR2019基于任意形状文本的鲁棒阅读挑战- RRC-ArT","authors":"Chee-Kheng Chng, Yuliang Liu, Yipeng Sun, Chun Chet Ng, Canjie Luo, Zihan Ni, Chuanming Fang, Shuaitao Zhang, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin","doi":"10.1109/ICDAR.2019.00252","DOIUrl":null,"url":null,"abstract":"This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 - 82.65%, ii) T2.1 - 74.3%, iii) T2.2 - 85.32%, iv) T3.1 - 53.86%, and v) T3.2 - 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"124","resultStr":"{\"title\":\"ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT\",\"authors\":\"Chee-Kheng Chng, Yuliang Liu, Yipeng Sun, Chun Chet Ng, Canjie Luo, Zihan Ni, Chuanming Fang, Shuaitao Zhang, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin\",\"doi\":\"10.1109/ICDAR.2019.00252\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 - 82.65%, ii) T2.1 - 74.3%, iii) T2.2 - 85.32%, iv) T3.1 - 53.86%, and v) T3.2 - 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.\",\"PeriodicalId\":325437,\"journal\":{\"name\":\"2019 International Conference on Document Analysis and Recognition (ICDAR)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"124\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Document Analysis and Recognition (ICDAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.2019.00252\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Document Analysis and Recognition (ICDAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2019.00252","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 124

摘要

本文报道了ICDAR2019关于任意形状文本的鲁棒阅读挑战- RRC-ArT，该挑战包括三个主要挑战:i)场景文本检测，ii)场景文本识别和iii)场景文本识别。本次比赛共收到来自46个独特团队/个人的78份参赛作品。各挑战的最高表现分数为:i) T1 - 82.65%， ii) T2.1 - 74.3%， iii) T2.2 - 85.32%， iv) T3.1 - 53.86%， v) T3.2 - 54.91%。除结果外，本文还详细介绍了ArT数据集、任务描述、评估指标和参与者方法。数据集、评估工具包以及结果都可以在挑战网站上公开获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT

This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 - 82.65%, ii) T2.1 - 74.3%, iii) T2.2 - 85.32%, iv) T3.1 - 53.86%, and v) T3.2 - 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2019 International Conference on Document Analysis and Recognition (ICDAR)

自引率

0.00%

发文量