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Text detection enables us to extract rich information from images. In thispaper, we focus on how to generate bounding boxes that are appropriate to grasptext areas on books to help implement automatic text detection. We attempt notto improve a learning-based model by training it with an enough amount of datain the target domain but to leverage it, which has been already trained withanother domain data. We develop algorithms that construct the bounding boxes byimproving and leveraging the results of a learning-based method. Our algorithmscan utilize different learning-based approaches to detect scene texts.Experimental evaluations demonstrate that our algorithms work well in varioussituations where books are roughly placed.

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