A Gateway API-Based Data Fusion Architecture for Automated User Interaction with Historical Handwritten Manuscripts

Abstract

To preserve handwritten historical documents, libraries are choosing to digitize them, ensuring their longevity and accessibility. However, the true value of these digitized images lies in their transcription into a textual format. In recent years, various tools have been developed utilizing both traditional and AI-based models to address the challenges of deciphering handwritten texts. Despite their importance, there are still several obstacles to overcome, such as the need for scalable and modular solutions, as well as the ability to cater to a continuously growing user community autonomously. This study focuses on introducing a new information fusion architecture, specifically highlighting the Gateway API. Developed as part of the μDoc.tS research program, this architecture aims to convert digital images of manuscripts into electronic text, ensuring secure and efficient routing of requests from front-end applications to the back end of the information system. The validation of this architecture demonstrates its efficiency in handling a large volume of requests and effectively distributing the workload. One significant advantage of this proposed method is its compatibility with everyday devices, eliminating the need for extensive computational infrastructures. It is believed that the scalability and modularity of this architecture can pave the way for a unified multi-platform solution, connecting diverse user environments and databases.

Keywords

• API
• handwritten text recognition
• keyword spotting models