Downtown Osaka Scene Text Dataset
| 言語 | 英語 |
|---|---|
| 種類 | 国際会議等の予稿 |
| 著者 | Masakazu Iwamura，Takahiro Matsuda，Naoyuki Morimoto，Hitomi Sato，Yuki Ikeda，Koichi Kise |
| 論文名 | Downtown Osaka Scene Text Dataset |
| 論文誌名 | Proc. 2nd International Workshop on Robust Reading (ECCV 2016 Workshops, Part I) |
| Vol. | 9913 |
| Series | Lecture Notes in Computer Science (LNCS) |
| ページ | pp.1-16 |
| 出版社 | Springer |
| 発表場所 | Amsterdam, Netherlands |
| 査読の有無 | 有 |
| 発表の種類 | 口頭発表 |
| 年月 | 2016年10月 |
| 分野 | パターン認識 |
| DOI | 10.1007/978-3-319-46604-0_32 |
| キー | Iwamura2016_ECCV (id=1528) |
要約
This paper presents a new scene text dataset named Downtown Osaka Scene Text Dataset (in short, DOST dataset). The dataset consists of sequential images captured in shopping streets in downtown Osaka with an omnidirectional camera. Unlike most of existing datasets consisting of scene images intentionally captured, DOST dataset consists of uncontrolled scene images; use of an omnidirectional camera enabled us to capture videos (sequential images) of whole scenes surrounding the camera. Since the dataset preserved the real scenes containing texts as they were, in other words, they are scene texts in the wild. DOST dataset contained 32,147 manually ground truthed sequential images. They contained 935,601 text regions consisting of 797,919 legible and 137,682 illegible. The legible regions contained 2,808,340 characters. The dataset is evaluated using two existing scene text detection methods and one powerful commercial end-to-end scene text recognition method to know the difficulty and quality in comparison with existing datasets.
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