Managing and finding books in large collections often leads to tedious manual work. In addition, sometimes books may be missing or misplaced. We can build a following algorithm:

• Given a bookshelf photo, it segments different book spines.
• On each book spine it locates the title (and splits in into separate characters).
• Text recognition algorithm transfers title photo into actual text which could be used to build a book database (alternatively, to find a certain book).

Good examples of text localization:

Tesseract OCR was used with Russian language flag. For training a cyrillic letters classifier we used dataset: CoMNIST.

1. Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading. Use text/non-text CNN to locate book spines, segment separate words and then predict words with CNN-RNN architecture.
2. A Framework for Recognition Books on Bookshelves. Use Canny edge map, Hough lines and calculate dominant vanishing point. Words are clustered in Canny edge map through dilation operator.
3. Viewpoint-Independent Book Spine Segmentation.
4. Book spine segmentation for various book orientations. Book spine edge map is segmented through morphological reconstruction and L0-gradient minimization. After that, SVM classifier recovers missing boundaries.
5. Some ideas were taken from this blog.