  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.
