SCALABLE INTELLIGENT DOCUMENT PROCESSING PIPELINES FOR MULTILINGUAL AND MULTI FORMAT ENTERPRISE DATA

Content of the Abstract: This paper presents a comprehensive overview of scalable intelligent document processing (IDP) pipelines designed for enterprise scale ingestion, classification, extraction, and understanding of multilingual and multi format data. With the accelerating volume of enterprise documents, traditional rule based approaches have become inadequate, prompting the integration of AI, machine learning (ML), and natural language processing (NLP) into automated document pipelines. Purpose: To explore scalable pipeline architectures that generalize across formats (PDF, images, typed text) and languages, making enterprise data processing more efficient and adaptable. Design/methodology/approach: A systematic review of existing methodologies is combined with an architectural exposition demonstrating modular pipeline design enhanced by OCR, layout parsing, multilingual NLP, and format aware routing. Findings: Modern IDP pipelines achieve significantly improved accuracy and throughput by employing hybrid OCR NLP stacks and transformer based contextual processors, addressing the linguistic diversity and heterogeneity of enterprise data. Practical implications: Implementations can be adopted by enterprises to automate high volume document workflows, minimize manual labor, and integrate processed data into downstream systems. Originality/value: This study synthesizes key advances and presents an integrated architectural model tailored for scalable, enterprise grade multilingual document processing.