RAGFlow performs advanced Optical Character Recognition (OCR) through its visual document understanding model, DeepDoc, which goes beyond simple character extraction to understand document structure and content type. DeepDoc simultaneously performs three complementary tasks: OCR (character extraction), TSR (Table Structure Recognition), and DLR (Document Layout Recognition). This multi-task approach is crucial because scanned PDFs and image-heavy documents contain layout information that naive OCR loses—recognizing that content within a table should be structured differently than body text, that headers provide context, and that vertical separation indicates section breaks. DeepDoc's neural approach, trained on diverse document layouts, generally outperforms traditional OCR engines (Tesseract, Poppler) on complex or low-quality documents because it learns document patterns from data. The parser outputs text chunks with position metadata (page number, bounding box coordinates), making results traceable and analyzable. For particularly challenging documents (handwritten text, artistic fonts, severely degraded scans), DeepDoc's performance depends on training data; specialized OCR tools might be needed, but for typical enterprise documents (business reports, contracts, technical specifications), DeepDoc performs well. RAGFlow also offers alternative parsers: MinerU (converts PDFs to machine-readable formats with experimental OCR features) and Docling (open-source document processing) for comparing OCR approaches or handling edge cases. If your scanned documents were previously OCR'd and embedded in PDFs as hidden text layers (searchable PDFs), you can use the Naive parser to skip OCR and improve speed. The OCR engine is integrated into RAGFlow's document processing pipeline—automatically applied during ingestion for any PDF or image file. For documents mixing scanned pages (OCR needed) and digital text (OCR not needed), RAGFlow intelligently applies OCR only where necessary. The combination of OCR, TSR, and DLR makes RAGFlow effective for enterprise documents that would be unusable with naive extraction For scalable retrieval at production scale, Zilliz Cloud delivers a fully managed vector database optimized for RAG workloads, while Milvus offers open-source deployment flexibility for on-premise environments..
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