Possibilities of Using OCR Technologies from Google for Recognition and Digitalization of Archive Documents

The relevance of the article. This article examines the significant capabilities of Google’s Optical Character Recognition (OCR) technologies, particularly Google Cloud Vision API, in transforming the way archival documents are digitized. The rapid evolution of digital technologies offers unprecedented opportunities not only for the preservation but also for the enhanced accessibility of historical documents, which are susceptible to degradation and potential loss over time. The purpose of the article. The primary objective of this study is to critically analyze and demonstrate the efficiency and effectiveness of employing Google Cloud Vision API to digitize diverse types of archival documents. The methodology. The approach taken involves a comprehensive evaluation of Google Cloud Vision API’s capability to accurately process and recognize text from a variety of archival materials, such as printed books, handwritten manuscripts, detailed maps, and other complex formats. The study methodically assesses the accuracy, processing speed, and cost-effectiveness of the OCR technology, comparing it to traditional digitization methods. Additionally, the research delves into the challenges of developing a user-friendly web interface that supports the seamless application of OCR technology. Conclusion. The study highlights significant advantages of integrating Google OCR technologies into archival practices. These technologies not only accelerate the digitization process but also considerably reduce operational costs and enhance the quality of the digital outputs. Furthermore, the research identifies the necessity for customized approaches to OCR processing, particularly for complex data types such as musical scores and detailed cartographic materials. This adaptability is crucial for preserving the nuances and accuracy of diverse archival contents. The findings suggest promising avenues for future research and development, indicating that further innovations in OCR technology could revolutionize the field of archival science. Expanded applications and implications. The study’s implications extend beyond mere digitization; it explores the potential for Google Cloud Vision API to facilitate advanced data analysis and retrieval techniques. By enabling more sophisticated search functionalities, such as context-aware searches and metadata generation, OCR technology can transform archival resources into dynamic databases that support academic research, historical inquiries, and public education. The practical significance. Building on the current findings, future research could explore the integration of OCR technology with blockchain to enhance the security and traceability of digitized documents. Exploring adaptive OCR techniques that can learn from user corrections and feedback may enhance the accuracy and reliability of digitization efforts, especially for documents with unusual fonts or degradation.