A well-maintained library is a valuable resource for educational institutions and research facilities. Efficient management of libraries requires a reliable system for cataloging books. This paper evaluates four prominent edge detectors - Canny, Sobel, Prewitt and Roberts, analyzing their performance using metrics such as mean square error (MSE), root mean square error (RMSE) and peak signal-to-noise ratio (PSNR). Why we study on the evaluation of edge detectors initiated from the studying of the University of Information Technology’s Library, which houses over 100,000 books. We aim to address the challenges of efficiently cataloging and retrieving books in the digital library of this extensive collection to collaborate with an IT research center for future improvements. In these days, optimizing edge detection for book spine recognition is essential for efficient cataloging, inventory management and quick retrieval in digital library applications. Thus, this paper presents a comparative analysis of the edge detection techniques to support in solving the challenges of a digital library in future. These techniques are within computer vision and image processing to enhance book spine detection accuracy, reduce manual labor and minimize errors in robotic libraries. Our evaluation results indicate that the Canny edge detector consistently outperforms the others, demonstrating superior performance with lower MSE, RMSE and higher PSNR values. Integrating the Canny edge detector into existing systems will enhance book organization and retrieval in the digital library. This accurate and noise-resistant detector addresses book spine segmentation issues by solving cataloging inefficiencies and improving overall management.