ICDAR2017 Robust Reading Challenge on Omnidirectional Video

M. Iwamura, Naoyuki Morimoto, Keishi Tainaka, Dena Bazazian, L. G. I. Bigorda, Dimosthenis Karatzas
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引用次数: 17

Abstract

Results of ICDAR 2017 Robust Reading Challenge on Omnidirectional Video are presented. This competition uses Downtown Osaka Scene Text (DOST) Dataset that was captured in Osaka, Japan with an omnidirectional camera. Hence, it consists of sequential images (videos) of different view angles. Regarding the sequential images as videos (video mode), two tasks of localisation and end-to-end recognition are prepared. Regarding them as a set of still images (still image mode), three tasks of localisation, cropped word recognition and end-to-end recognition are prepared. As the dataset has been captured in Japan, the dataset contains Japanese text but also include text consisting of alphanumeric characters (Latin text). Hence, a submitted result for each task is evaluated in three ways: using Japanese only ground truth (GT), using Latin only GT and using combined GTs of both. Finally, by the submission deadline, we have received two submissions in the text localisation task of the still image mode. We intend to continue the competition in the open mode. Expecting further submissions, in this report we provide baseline results in all the tasks in addition to the submissions from the community.
ICDAR2017全向视频鲁棒阅读挑战
介绍了ICDAR 2017全向视频鲁棒阅读挑战赛的结果。本次比赛使用的是在日本大阪用全向相机拍摄的大阪市中心场景文本(DOST)数据集。因此,它由不同视角的连续图像(视频)组成。将序列图像作为视频(视频模式),准备了定位和端到端识别两个任务。将它们作为一组静止图像(静止图像模式),准备了定位、裁剪词识别和端到端识别三个任务。由于数据集是在日本捕获的,因此数据集包含日文文本,但也包括由字母数字字符组成的文本(拉丁文本)。因此,每个任务提交的结果以三种方式进行评估:仅使用日语的基础真值(GT),仅使用拉丁语的GT,以及使用两者的组合GT。最后,在提交截止日期前,我们在静止图像模式的文本定位任务中收到了两份提交。我们打算以开放的方式继续比赛。期待更多的提交,在本报告中,我们提供了所有任务的基线结果以及来自社区的提交。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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