This article provides a theoretical analysis and comparative evaluation of four approaches to document workflow automation. Using the document workflow of a large company, OFFICEMAG LLC, with a total of approximately 7,000 employees and over 112 suppliers with unique templates, it is demonstrated that regular expressions and contextual logic provide the highest stability, transparency, and cost-effectiveness when working with unstructured and voluminous documents. The model proposed in this article requires no training, requires no ML engineers, is license-independent, and adapts to new templates in minutes. A comparison of all models showed that regular expressions and contextual logic are the most effective approach to automating business documents.
document automation, regular expressions, document classification, contextual logic, business logic, machine learning, RPA, large businesses, logistics, method comparison
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