LaSEEnem: Development and Quality Evaluation of a Museum Relic Recognition Application Using Convolutional Neural Networks

Abstract

Culture and heritage are important aspects of historical places like Vigan City, which is surrounded by museums to preserve and enliven the city’s antiquity. In this study, the researchers investigated the difficulties that the museums of Vigan City encounter: traffic congestion and lengthy visitor lines, and the congested area that causes accidents and destroys the artifacts kept. Due to the conventional nature of the exhibition setup and the traditional communication style used for presenting the collections, museums had to keep their audiences engaged by closely examining innovative ways to employ technology to enhance their art collections. The researchers intended to develop LaSEEnem, a museum relic recognition application for selected museums of Vigan City. The researchers used descriptive and developmental designs to gather information needed to develop LaSEEnem. Furthermore, to develop the proposed application, the researchers used the Rapid Application Development (RAD) Model and the Convolutional Neural Network (CNN). The developed system was evaluated using the ISO 25010 evaluation tool. The results reflected a mean of 4.26, indicating that the system is Highly Acceptable as a digital tool intended to support artifact recognition and information access during museum visits. This study contributes to the growing body of research on AI-assisted cultural heritage applications by demonstrating the feasibility of integrating CNN-based image recognition with a mobile museum guide evaluated through the ISO 25010 quality model.