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Text Detection on Roughly Placed Books by Leveraging a Learning-based Model Trained with Another Domain Data

2020-06-26 05:53:23
Riku Anegawa, Masayoshi Aritsugi
arXiv_CV
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2006.14808

PDF

https://arxiv.org/pdf/2006.14808.pdf

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