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Physical library collections are valuable and long standing resources forknowledge and learning. However, managing books in a large bookshelf andfinding books on it often leads to tedious manual work, especially for largebook collections where books might be missing or misplaced. Recently, deepneural models, such as Convolutional Neural Networks (CNN) and Recurrent NeuralNetworks (RNN) have achieved great success for scene text detection andrecognition. Motivated by these recent successes, we aim to investigate theirviability in facilitating book management, a task that introduces furtherchallenges including large amounts of cluttered scene text, distortion, andvaried lighting conditions. In this paper, we present a library inventorybuilding and retrieval system based on scene text reading methods. Wespecifically design our scene text recognition model using rich supervision toaccelerate training and achieve state-of-the-art performance on severalbenchmark datasets. Our proposed system has the potential to greatly reduce theamount of human labor required in managing book inventories as well as thespace needed to store book information.

Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading