Vision-Language OCR for PDFs and Spreadsheets Elevates Multimodal Q&A from 45% to 75%

Vision-Language OCR for PDFs and Spreadsheets Elevates Multimodal Q&A from 45% to 75%

The Challenge

The system faced significant limitations in handling multimodal documents:

  • PDFs: No text extraction was available for non-editable and scanned PDFs, making Q&A impossible and highlighting a gap in AI for PDFs within broader multimodal document processing.
  • Visuals: Images, figures, and graphs embedded in documents were ignored.
  • Other formats: Spreadsheets, PowerPoint decks, and Word documents often failed to return correct answers, underscoring the need for stronger AI for spreadsheets.

Hypothesis

If we leverage document layout intelligence combined with vision-language models for enterprise documents, we can:
  • Extract and structure text more effectively from complex document formats using OCR and layout-aware extraction.
  • Generate meaningful summaries of images, graphs, and tables as part of multimodal document processing.
  • Improve accuracy and consistency of Q&A across diverse content types with targeted AI for PDFs and AI for spreadsheets enhancements.

Execution

  • Integrated vision-language models for enterprise documents to interpret document layouts and optimize text parsing via external libraries.
  • Applied advanced OCR and layout-aware extraction methods for non-editable PDFs, strengthening AI for PDF capabilities.
  • Added summarization of visual elements (tables, charts, images, and figures) with context awareness, and reinforced AI for spreadsheets handling for tabular reasoning.

Outcomes

  • Structured text extraction significantly improved system answer quality through effective multimodal document processing.
  • Summaries of visuals enhanced retrieval and Q&A accuracy, bridging gaps in multimodal understanding with vision-language models for enterprise documents.
  • Overall performance improved from 45% to 75% on multimodal Q&A tasks.
  • Achieved nearly 40% improvement for spreadsheet (XLSX) queries over the baseline.

Project Highlights

67%

increase in Q&A task performance.