Оптичне розпізнававання тексту в історичних документах: ключові проблеми та іноваціні рішення

Abstract

Optical Character Recognition (OCR) of historical documents is critically important not only for preserving cultural heritage and providing access to archival materials, but also for the modern printing industry, which increasingly turns to digitized classical texts and the reprinting of rare editions. However, this task is complicated by a number of specific challenges: from physical degradation and non-standard fonts in old prints to the diversity of handwriting in manuscripts. This paper analyzes the key difficulties of OCR applied to historical materials, examines current innovative approaches (specialized neural networks, page layout segmentation, language models for post-processing), and outlines future research directions relevant to both archival science and publishing. It is shown that combining modern machine learning technologies with an understanding of the particular features of historical sources significantly improves recognition quality and opens new possibilities for reprinting and editorial work with classical texts.