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Multimodal technology for searching and clustering weakly structured text-graphic documents

Year: 2024

Type: article

Abstract: The problem of searching and clustering poorly structured text-graphic documents (TGD) using neural network technologies is addressed. A multimodal approach to building a system for searching and analyzing TGD is proposed, utilizing a hybrid criterion to compare text and graphic fragments within the analyzed documents. Integrating processes for identifying text-graphic fragments in electronic documents based on image characteristics and text signatures (keywords) is described. A method for clustering and indexing TGD is introduced, leveraging the SOINN algorithm, linguistic descriptors, and a modular file array processing system. This method enables the formation of a dynamic cluster structure for TGD and the creation of unified indexes. The testing results and practical recommendations for implementing neural network models in TGD search and clustering are presented.

Source: Bionics of Intelligence

DOI: 10.30837/bi.2024.2(101).01

Authors: S.G. Udovenko, Ye.M. Hrabovskyi, D.O. Donskyi, L.E. Chala

Institutions: Simon Kuznets Kharkiv National University of Economics, Kharkiv National University of Radio Electronics

Language: English

Topic: Handwritten Text Recognition Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4409342054

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