Library Automation System: Book cover recognition using deep learning

With thousands of books across hundreds of disciplines present in a library, manual allocation of books is both time consuming, human intensive, as well as inefficient in terms of monetary costs. The presence of manual labour while providing employment to a section of society tends to slow down the process of lending in addition to increasing the probability of errors. The proposed research work attempts at automating the library book management system there by reducing latency and long queues as well as the potential of making mistakes in the distribution of books. Recent advancements of deep neural networks have improved textual recognition in natural scenes. The system proposed employs a neural network model for text detection and recognition, having the potential to reduce and even eliminate manual labour performed by the staff. This paper attempts to investigate neural networks for the application of library management by proposing our own Optical Character Recognition (OCR) and text matching algorithms. This task further introduces challenges such as getting the exact name of the book as well as dealing with distorted images and varied backgrounds. The system thus developed performs very well with book covers producing results over 60% in accuracy against current models that provide about 40% thereby making the system achieve state of the art performance on book cover information retrieval.