Enhancing Symbol Recognition in Library Science via Advanced Technological Solutions

Abstract

This research introduces an artificial intelligence-based strategy for improving symbol recognition within the field of library science, concentrating on the creation and application of sophisticated technological solutions. Consistent with the objectives of the CHANGES project—Cultural Heritage Active Innovation for Sustainable Society, which focuses on the enhancement and management of cultural heritage through a multidisciplinary and interinstitutional approach—this strategy employs convolutional neural networks (CNNs) for accurate symbol classification. A CNN model was developed using an extensive dataset comprising over 6000 symbols, implementing meticulous preprocessing, feature extraction, and supervised learning protocols. The methodological pipeline incorporates advanced image segmentation techniques to isolate symbols from complex manuscripts, followed by data augmentation to enhance model resilience. The system is supported by a high-performance computing framework to manage large datasets efficiently, thereby facilitating more precise identification and analysis. This integration of machine learning techniques, exhaustive data management, and computational capabilities significantly advances existing symbol recognition methodologies, providing scholars with a potent tool for assisting in the classification and interpretation of historical symbols. The findings corroborate the potential of AI-enhanced symbol recognition in contributing to the broader objectives of computational library science and historical research.

Keywords

• artificial intelligence
• symbol recognition
• convolutional neural networks
• historical manuscripts
• library science
• computational analysis