This paper presents a method for the automated attribution of archival handwritten letters based on a Siamese neural network, addressing a key challenge in digital humanities – the authentication of historical documents. The research is motivated by the mass digitization of 17th to 19th-century archives, where attribution is often hindered by incomplete or inaccurate metadata about the authors.

The method is designed for real-world document collections and accounts for challenges typical of archival materials: poor-quality scans, significant handwriting variation, and substantial class imbalance (from 1 to over 50 samples per author). The use of a Siamese network architecture enables the extraction of discriminative vector representations (embeddings). Based on these embeddings, the method not only classifies documents by known authors but also effectively identifies manuscripts that do not match any known author in the archive. This significantly narrows down the pool of candidates for subsequent expert verification.

The study introduces a data preprocessing algorithm and provides a comparative analysis of two approaches to text analysis: at the image fragment level (300×300 px) and at the individual text line level. The developed tool offers archivists and philologists an effective solution for the preliminary sorting and attribution of handwritten documents large collections.

The problem of finding the most relevant documents as a result of an extended and refined query is considered. For this, a search model and a text preprocessing mechanism are proposed, as well as the joint use of a search engine and a neural network model built on the basis of an index using word2vec algorithms to generate an extended query with synonyms and refine search results based on a selection of similar documents in a digital semantic library. The paper investigates the construction of a vector representation of documents based on paragraphs in relation to the data array of the digital semantic library LibMeta. Each piece of text is labeled. Both the whole document and its separate parts can be marked. The problem of enriching user queries with synonyms was solved, then when building a search model together with word2vec algorithms, an approach of "indexing first, then training" was used to cover more information and give more accurate search results. The model was trained on the basis of the library's mathematical content. Examples of training, extended query and search quality assessment using training and synonyms are given.