Reproduce “Generating Synthetic Invoices via Layout-Preserving Content Replacement” on OpenResearch
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About this paper
Bevin V, Ananthakrishnan P V, Ragesh KR, Sanjay M, Vineeth S, Bibin Wilson
BEO AI researchers developed SynthID, a multi-stage pipeline that generates high-fidelity synthetic invoice documents with perfectly matched structured data. This system uses OCR to extract original layouts, Large Language Models to generate new, contextually relevant content, and computer vision techniques for inpainting and dynamic text rendering, addressing data scarcity and privacy concerns for training document intelligence models.
Key insights
- Leveraging OCR to extract precise text bounding boxes provides an accurate spatial 'scaffold' for layout preservation during content replacement.
- Large Language Models can effectively generate diverse and contextually plausible replacement content for various invoice fields, ensuring semantic realism while anonymizing data.
- Combining inpainting with dynamic text rendering allows for seamless visual integration of new content, ensuring synthetic documents maintain professional appearance and visual integrity.
Summary by alphaXiv.
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