This paper addresses the problem of accelerating the construction of accurate editorial annotations for handwritten archival texts within an incremental training cycle based on weak transcription. Unlike our previously published results, the present work focuses on integrating automatic post-correction of weak transcriptions using large language models (LLMs). We propose and implement a protocol for applying LLMs at the line level in a few-shot setup with carefully designed prompts and strict output format control (preservation of pre-reform orthography, protection of proper names and numerals, prohibition of structural changes to lines). Experiments are conducted on the corpus of diaries by A.V. Sukhovo-Kobylin. As the base recognition model, we use the line-level variant of the Vertical Attention Network (VAN). Results show that LLM post-correction–exemplified by the ChatGPT-4o service–substantially improves the readability of weak transcriptions and significantly reduces the word error rate (in our experiments by about −12 percentage points), without degrading the character error rate. Another service tested, DeepSeek-R1, demonstrated less stable behavior. We discuss practical prompt engineering, limitations (context length limits, risk of “hallucinations”), and provide recommendations for the safe integration of LLM post-correction into an iterative annotation pipeline to reduce expert annotators’ workload and speed up the digitization of historical archives.

This article is devoted to the study of the possibilities of optimizing the operability and improving the efficiency of complex multifunctional software systems using the example of free automated library and information systems (hereinafter - ALIS).

By 2023, the world has accumulated valuable experience in the creation and operation of integrated ALIS of various scales and purposes, but the issues of improving their design solutions remain relevant. First of all, this concerns the need to optimize the structure of the source code in order to increase its readability and maintainability, reduce the execution time of individual functional modules, and reduce the amount of RAM used.

As part of the study, a comparative analysis of the source codes of several existing open source databases implemented in various programming languages was carried out. The main approaches to the design of the code structure were studied, the most frequently used algorithms and patterns were identified. To assess the degree of optimization of the source code, a set of indicators was developed, including an assessment of the structure, readability, modularity and other characteristics. On this basis, individual code fragments were compared before and after the use of well-known refactoring techniques.

As a result of the work carried out, it was possible to identify the most common errors and shortcomings in the structuring of the source codes of the ALIS, to determine the main directions of their optimization. Data has been obtained on the possible reduction of testing and technical support costs by improving the quality of source codes.

A new precise MCE algorithm for extracting the main content from news websites is presented. The proposed algorithm uses analysis of the Document Object Model (DOM) structure and content density metrics to identify and extract the informational core of a web page. The implemented approach combines three key features: the maximum number of direct child elements containing text, the maximum textual content without child elements containing text, and the closest position to the average node depth. The algorithm demonstrated superior performance compared to existing solutions such as Boilerpipe and Readability, achieving 99.96% precision, 99.69% recall, and 99.80% F1-score on a comprehensive dataset of 500 diverse web pages. Its language-independent design makes the algorithm particularly effective for extracting multilingual content, including languages with complex structures such as Arabic.