Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading
This addresses the tedious manual work in library book management, though it is an incremental application of existing methods to a new domain.
The paper tackles the problem of managing and finding books in large library collections by developing a system that uses richly supervised deep scene text reading to identify books on shelves, achieving state-of-the-art performance on benchmark datasets.
Physical library collections are valuable and long standing resources for knowledge and learning. However, managing books in a large bookshelf and finding books on it often leads to tedious manual work, especially for large book collections where books might be missing or misplaced. Recently, deep neural models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have achieved great success for scene text detection and recognition. Motivated by these recent successes, we aim to investigate their viability in facilitating book management, a task that introduces further challenges including large amounts of cluttered scene text, distortion, and varied lighting conditions. In this paper, we present a library inventory building and retrieval system based on scene text reading methods. We specifically design our scene text recognition model using rich supervision to accelerate training and achieve state-of-the-art performance on several benchmark datasets. Our proposed system has the potential to greatly reduce the amount of human labor required in managing book inventories as well as the space needed to store book information.