Open Source
TAT-DQA
TAT-DQA: A Document Visual Question Answering (VQA) Dataset, aiming to answer questions over visually-rich documents with a hybrid of Tabular and Textual Content in Finance
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About TAT-DQA
TAT-DQA is a document visual question answering dataset designed for answering questions over visually-rich financial documents. It combines tabular and textual content to benchmark models in understanding hybrid document layouts.
Best for
Researchers and developers building document understanding systems for financial reports and invoices
Use cases
- Training and evaluating document VQA models on financial reports
- Extracting structured answers from tables and text in invoices or filings
- Benchmarking multimodal understanding of scanned or digital documents
Pros
- Specialized for finance with real-world tabular and textual content
- Community-maintained, enabling open research and reproducibility
Cons
- Domain-specific to finance, limiting generalizability to other document types
- Requires domain knowledge for effective use and interpretation of results
Key facts
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