  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.
