Smart Library: Identifying Books on Library Shelves Using Supervised Deep Learning for Scene Text Reading
Manual inventory management in a library is by far arduous. Automation of book inspection can be achieved by using a simple camera based system that can recognize book spines in a book shelf. The book spines contain printed information such as title, author and publisher name, which can be extracted and verified with the library's database. Book spines can be segmented by detecting their rectangular boundaries which appear as straight lines. Line detection using hough transform and line segment detector may result in spurious boundaries due to the presence of long titles or graphics on the book spine. In this paper, we propose a technique to improve book spine border detection by devising set of constraints based on structural properties that can be used to filter the detected line segments so as to obtain book spine boundaries. The segmented book spines are binarized to extract the printed information such as title, author and publisher name. The text is recognized using Tesseract Optical Character Recognition Engine. The proposed algorithm was tested successfully on book shelf images with vertically oriented, uniformly inclined and multi-oriented book spines.