Our research spans multiple areas of document AI, all focused on solving real-world challenges in financial document processing.
Advanced models for parsing complex document layouts, handling multi-column formats, and understanding document structure.
Supporting 100+ languages, mixed-language documents, and low-resource languages.
State-of-the-art table detection and structure recognition for complex, nested, and spanning tables.
Semantic understanding, entity extraction, and reasoning over document content.
Robust processing of degraded scans, handwritten documents, and poor image quality.
Cross-field consistency checking, anomaly detection, and confidence estimation.
We believe in collaborative research to push the field forward.
We collaborate with leading universities on document AI research, contributing to open benchmarks and shared datasets.
Working with enterprise partners to solve real-world financial document processing challenges at scale.
Contributing models, datasets, and tools to the open source community through HuggingFace and GitHub.
Try our models directly on HuggingFace with interactive demos.
Explore on HuggingFaceVisit the main Hyperbots research page for papers, publications, and collaborations.
View ResearchPartner with us on custom research projects and enterprise solutions.
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