Text Detection on Roughly Placed Books by Leveraging a Learning-based Model Trained with Another Domain Data
This addresses text detection for books in non-ideal placements, but it is incremental as it adapts existing methods rather than introducing a new approach.
The paper tackled the problem of detecting text on roughly placed books by leveraging a pre-trained learning-based model from another domain, and the result was that their algorithms performed well in various situations.
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.