Document OCR for RAG & Vector Search
Extract clean Markdown and structured text from scanned documents to eliminate retrieval noise in RAG systems.
Drop your file here
PDF, PNG, JPG, WebP, BMP
How Poor OCR Corrupts RAG Pipelines
- Garbled characters and broken words degrade embedding vectors and cause similarity search failures.
- Unstructured OCR dumps split table rows and headings awkwardly across chunk boundaries.
- Repetitive page headers and footers get embedded repeatedly, polluting search results.
- Non-Latin languages like Urdu and Arabic suffer severe character fragmentation in standard OCR tools.
Chunk-Ready Markdown Export
Outputs clean Markdown with proper H1-H6 headers and table grids for semantic chunking.
High Character Fidelity
Accurately extracts text across 100+ languages to ensure reliable dense and sparse embeddings.
Header & Footer Isolation
Removes distracting running headers and page numbers so chunks contain only relevant content.
Structured JSON Output
Access bounding boxes, word confidences, and page-level metadata for advanced retrieval.
Developer API for Ingestion
Automate document ingestion into LangChain, LlamaIndex, and custom pipelines via REST API.
Frequently Asked Questions
Why is high OCR accuracy critical for RAG systems?
Embedding models rely on precise text representations. Misspelled words or scrambled characters create faulty vector embeddings, causing retrieval failures and model hallucinations.
Can I use FastOCR in an automated ingestion pipeline?
Yes. FastOCR provides a REST API that returns clean text, Markdown, or structured JSON for automated RAG indexing.
What file formats can FastOCR ingest for RAG?
You can upload PDF files up to 1GB, as well as PNG, JPG, WebP, and HEIC image formats.
Need Multi-Page PDF OCR or Batch Processing?
Extract text from scanned PDFs, translate into 65+ languages, or process up to 25 files at once on the main FastOCR engine.
Explore Main FastOCR Engine →