Label Text Recognition Using Image Processing Techniques and Convolutional Neural Networks for Smart Library

The library is a common and important public facility in contemporary society for information dissemination and cultural communication. A library usually holds thousands of books and each book is assigned to a unique position so that the visitors can find the books easily by checking the database of the library. However, the misplaced books bring trouble for readers to find them. Therefore, finding out these misplaced books and rearranging them is one of the important jobs for librarians. In this thesis, a convolutional-neural-network-based book label recognition algorithm is proposed to help librarians finding out the misplaced books by scanning the book labels. The algorithm is divided into two parts: the first part applies image processing techniques to extract the characters of the labels attached to each book from the images of the bookshelves. The second part uses convolutional neural networks (CNNs) to train a classifier for recognizing characters. In this part, a CNN architecture with four convolutional layers is designed to train classifiers for classifying characters and numbers that are used for recognition of the text. Finally, the algorithm combines the results of the two parts to recognize the text of a label by classifying each character or number of the text