ADAPTIVE DOCUMENT CLASSIFICATION USING HYBRID NLP AND COMPUTER VISION MODELS IN ENTERPRISE WORKFLOWS
Keywords:
Hybrid Document Classification, NLP, Computer Vision, Multimodal Learning, Enterprise WorkflowsSynopsis
Purpose: This paper examines how hybrid models combining Natural Language Processing (NLP) and Computer Vision (CV) enable adaptive document classification in enterprise workflows, improving accuracy and robustness across diverse document types. Design/methodology/approach: We propose a multimodal classification framework that integrates text and visual features extracted through NLP and CV networks. The framework is evaluated against traditional unimodal classification approaches. Findings: The hybrid approach achieves higher classification performance and adaptability across varying document structures compared to single modality methods. Practical implications: Enterprises can enhance automation pipelines for document processing—such as indexing, routing, and retrieval—by deploying hybrid models. Originality/value: This study synthesizes prior research and proposes a practical architecture for real world enterprise classification tasks, demonstrating the benefits of multimodality.
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