Multimodal RAG: PDFs, Tables, Images and OCR
How to build retrieval pipelines that preserve text, tables, images, page structure, and citations from complex documents.
Why ordinary text extraction is not enough
Real PDFs often contain tables, scanned pages, charts, images, headers, footers, and multi-column layouts. Flattening everything into plain text can destroy relationships that matter to the answer.
A multimodal ingestion pipeline
Separate document detection, OCR where required, text extraction, table extraction, image handling, chunking, metadata creation, and indexing. Preserve page numbers, section headings, and source identifiers so answers can cite the original document.
Tables need special treatment
A table should retain row and column relationships. Consider storing a structured representation alongside a textual rendering for retrieval. For calculations, route relevant tables to deterministic code when possible rather than asking an LLM to perform arithmetic from flattened text.
Images and OCR
Use OCR for scanned documents and image understanding for diagrams or screenshots where visual content carries meaning. Validate OCR on representative documents because recognition errors can become retrieval errors.
Citations and evaluation
Create test cases for text-only pages, tables, scans, mixed layouts, and image-heavy documents. Evaluate extraction separately from retrieval and answer quality.
Keep learning
Apply the concepts in a small project, measure the result, document the trade-offs, and explore related TechStudio resources.
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