Mohammad Zubair Khan

Papers

Total Citations

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About

Mohammad Zubair Khan is a leading researcher in document intelligence and natural language processing, with a focus on multi-modal approaches for real-world document management. His most cited work, "A Multi-Modal Approach to Digital Document Stream Segmentation for Title Insurance Domain" (2022, 21 citations), addresses a critical challenge in the digital transformation of corporate and public sectors: the segmentation of heterogeneous document streams. Khan’s key contribution lies in developing methods to automatically parse and organize scanned document batches—known as digital packages—by integrating visual and textual features. This work has significant implications for industries like title insurance, where accurate document classification is essential for legal and operational efficiency. Beyond this, Khan’s research explores the intersection of machine learning and document processing, aiming to reduce manual labor and error in digital archiving. With a growing citation impact, his contributions are shaping how organizations manage vast repositories of unstructured data. Khan’s work is particularly notable for its practical, domain-specific applications, bridging the gap between academic AI research and industry needs. For students and researchers, his studies offer a compelling example of how multi-modal learning can solve tangible problems in document-heavy sectors.