###### 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To preserve handwritten historical documents, libraries are choosing to digitize them, ensuring their longevity and accessibility.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 \(\upmu\)Doc.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. API; handwritten text recognition; keyword spotting models +
Footnote †: journal: Interface  
0000-0001-5000-0000-0000-0000-0000-0000-00000-0000-0000-0000-0000-0000-00000-00  
of renowned works to the general public [4]. With the progress in the fields of Artificial Intelligence (AI) and machine learning (ML), researchers are exploring AI-based methods to automate text generation, focusing on ancient languages like Latin [5], Greek [6], Hebrew [7], Japanese [8], Tulu [9], or multi-language approaches [10]. Collaboration with computing scientists has led to the successful development of customized Handwritten Text Recognition (HTR) solutions [11]. The Homer Multitext project was the result of many years of study and digitization of manuscripts that brought [PERSON]'s Iliad to the modern world, and it was scattered throughout Europe in museums, monasteries, and private collections [12]. The platform is being enhanced with new features up to this day and has spawned Computer Science and Computer Engineering projects related to the manuscripts, such as the identification of the number of hands that wrote them using an automatic, computerized writer identification system [13]. The HisDoc project in 2012 was one of the pioneering efforts to digitize historical documents using image analysis and text recognition [14]. HisDoc 2.0 improved digital paleography techniques and introduced features like text localization and script differentiation [15]. DivaDesk, a virtual workspace, was a key component. Challenges remain in digitizing high-quality images and integrating historical sources effectively [16]. The EU-funded IMPACT initiative enhanced digitization capabilities in European libraries [17]. Transkribus offers automated recognition and transcription of historical texts, increasing accessibility. It originated from the EU-funded 'Transcriptorium' project and is widely used in the cultural heritage sector, with 1700 regular monthly users [18]. Despite their great importance, all these efforts to digitize historical manuscripts face challenges in accurately transcribing handwritten text. Handwritten Text Recognition (HTR) is complex due to diverse writing styles and unique characters in different languages. Scalable tools are needed to incorporate these algorithms, especially with increasing digitization [19]. Robust infrastructure is essential for providing access to historical documents while effectively protecting them from cyberattacks, which have been on the rise in recent years, focusing on important online archives [20, 21].