论文标题
通过利用培训其他域数据训练的基于学习的模型，对大致放置的书籍进行了文本检测
Text Detection on Roughly Placed Books by Leveraging a Learning-based Model Trained with Another Domain Data
论文作者
论文摘要
文本检测使我们能够从图像中提取丰富的信息。在本文中，我们专注于如何生成适合掌握书籍文本区域的边界框，以帮助实施自动文本检测。我们试图通过在目标域中使用足够数量的数据来培训基于学习的模型，而是要利用它，而该模型已经接受了其他域数据的培训。我们开发算法，通过改善和利用基于学习的方法的结果来构建边界框。我们的算法可以利用不同的基于学习的方法来检测场景文本。实验评估表明，在大致放置书籍的各种情况下，我们的算法效果很好。
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.
