Analysis of the possibilities application of the Vision Grid Transformer model for document structure analysis of ukrainian accounting documents

In the context of ongoing digital transformation, the need for automation of accounting document processing is steadily increasing, particularly in Ukraine, where a significant portion of primary documentation still exists in paper form or as scanned images. Effective information extraction from such documents requires the application of advanced artificial intelligence techniques, especially deep learning and multimodal data analysis. This paper explores the applicability of the Vision Grid Transformer (VGT) architecture for analyzing the layout structure of Ukrainian accounting documents.The VGT model combines two information streams – a visual stream (based on the Vision Transformer, ViT) and a text-spatial stream (based on the Grid Transformer, GiT), enabling comprehensive document representation both in terms of visual layout and semantic content. The model's flexibility is further enhanced by pretraining methods such as Masked Grid Language Modeling (MGLM) and Segment Language Modeling (SLM), which help capture both local and global contextual dependencies among textual elements. The study emphasizes the specific challenges of adapting VGT to the Ukrainian language and accounting domain. These include the lack of high-quality publicly annotated Ukrainian datasets, the necessity of accurate optical character recognition (OCR) for Cyrillic scripts, issues related to handwritten text recognition (HTR), and the complexity of domain-specific terminology and abbreviations used in accounting.