Integration of Document Digitalization Software in the Electronic System of the Archive

The relevance of the research. The modern world is rapidly moving to a digital format for storing and processing information. Archives, like any other organization, must adapt to the trend by introducing digital technologies to preserve their documents. Choosing the appropriate means of digitizing documents in archives using modern solutions will allow quickly and efficiently preserve large amounts of information. Researching, selecting, and using appropriate document classification algorithms will help speed up the process of sorting documents into the right categories. The purpose of the article is to analyze the functionality of document digitization software for their integration into electronic archive systems. The methodology. To accomplish this objective, various theoretical and scientific research approaches were utilized, including analysis, synthesis, induction, and deduction. The results. The article highlights modern technologies and software designed for document digitization, explores existing problems that arise during document digitization and classification and developed recommendations for the integration of digitization systems into the archive. The scientific novelty. The article highlights and summarizes recent scientific publications related to the development and enhancement of document digitization tools. It reviews modern research conducted by scientists in the field of handwritten text and image recognition. Additionally, it presents general methodological recommendations for integrating digitization systems into archives. The practical significance. The article will be useful to scientists, software developers and archive workers. The presented research results are relevant and structured. This scientific article can become the basis for further research into document digitization systems. The conclusion. A significant breakthrough occurred with the development of optical character recognition (OCR) technology. Building upon this, more sophisticated algorithms have been developed for recognizing handwritten text in various languages. Presently, there is ongoing research focused on achieving high-quality digitization of images, enabling efficient search capabilities within vast collections of digitized materials. Artificial intelligence has greatly simplified and accelerated work in this direction, but challenges persist with recognition accuracy. Currently, research efforts continue, and a reference database is being developed to facilitate greater automation of the classification process. The formulated methodology for integrating digitization tools into the archive enhances comprehension of the process and simplifies the preparatory tasks associated with integration.