Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading

Physical library collections are valuable and long standing resources for knowledge and learning. However, managing and finding books or other volumes on a large collection of bookshelves often leads to tedious manual work, especially for large collections where books or others might be missing or misplaced. Recently, deep neural-based models have been successful in detecting and recognizing text in images taken from natural scenes. Based on this, we investigate deep learning for facilitating book management. This task introduces further challenges including image distortion and varied lighting conditions. We present a library inventory building and retrieval system based on scene text reading. We specifically design our 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 manual labor required for managing book inventories.