Method for automatic document verification for compliance with regulatory requirements

This paper focuses on developing an efficient method for automatically verifying docu-ment formats, ensuring they meet specific formatting standards. Existing approaches utilizing rule-based systems and machine learning techniques are reviewed and analyzed. A modified method that integrates both structural and linguistic checks is proposed. A comparative anal-ysis of the proposed method against existing approaches is conducted. Potential directions for further research are proposed as well. The study reviews current approaches, including rule-based systems and machine learning techniques, evaluating their effectiveness in detecting formatting inconsistencies. While rule-based methods offer precision and transparency, they are limited in adaptability to complex document structures. Conversely, machine learning techniques demonstrate greater flexibility but often require extensive labeled datasets and struggle with interpretability. To address these challenges, a hybrid approach is proposed, combining structural analysis with linguistic verification. This method integrates predefined formatting rules with natural lan-guage processing methods to enhance accuracy and adaptability. The proposed system is implemented using Word API for structural verification, while LanguageTool API is used to analyze textual aspects to identify stylistic and linguistic devia-tions. Key formatting aspects evaluated include font consistency, margins, line spacing, para-graph alignment, and numbering styles. Additionally, NLP responses are filtered using Le-venstein distance to prevent false and senseless results.