2020年6月26日
Text Detection on Roughly Placed Books by Leveraging a Learning-based Model Trained with Another Domain Data
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- 記述言語
- 英語
- 掲載種別
- 機関テクニカルレポート，技術報告書，プレプリント等
Text detection enables us to extract rich information from images. In this
paper, we focus on how to generate bounding boxes that are appropriate to grasp
text areas on books to help implement automatic text detection. We attempt not
to improve a learning-based model by training it with an enough amount of data
in the target domain but to leverage it, which has been already trained with
another domain data. We develop algorithms that construct the bounding boxes by
improving and leveraging the results of a learning-based method. Our algorithms
can utilize different learning-based approaches to detect scene texts.
Experimental evaluations demonstrate that our algorithms work well in various
situations where books are roughly placed.
paper, we focus on how to generate bounding boxes that are appropriate to grasp
text areas on books to help implement automatic text detection. We attempt not
to improve a learning-based model by training it with an enough amount of data
in the target domain but to leverage it, which has been already trained with
another domain data. We develop algorithms that construct the bounding boxes by
improving and leveraging the results of a learning-based method. Our algorithms
can utilize different learning-based approaches to detect scene texts.
Experimental evaluations demonstrate that our algorithms work well in various
situations where books are roughly placed.
- リンク情報
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- arXiv
- http://arxiv.org/abs/arXiv:2006.14808
- Arxiv Url
- http://arxiv.org/abs/2006.14808v1
- Arxiv Url
- http://arxiv.org/pdf/2006.14808v1 本文へのリンクあり
- ID情報
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- arXiv ID : arXiv:2006.14808