RAG ingestion decision tool

EasyOCR vs Docling: pick your extraction engine

Scanned PDFs are the hardest mile of a RAG pipeline. This tool compares the two most common open-source extraction paths, scores which one fits your corpus with a five-question quiz, and animates the full scan, OCR, chunk, and embed flow so you know exactly what you are choosing between.

Tradeoff matrix

Five dots is best-in-class. EasyOCR is a lightweight text detector that gives you raw strings fast; Docling is a full document-understanding stack that preserves structure at the cost of heavier compute.

DimensionEasyOCRDoclingWhat it means for RAG
Layout understanding Bounding boxes only Reading order, headings, columnsStructure-aware chunks retrieve dramatically better on reports and papers.
Table extraction Cells flatten to text soup TableFormer model, real rows and cellsFinancial and scientific corpora live or die on table fidelity.
Speed and footprint Light, fast on GPU or CPU Multiple models per page, heavierAt millions of pages, per-page latency is your cloud bill.
Output format Text plus box coordinates Markdown, JSON, DocTagsMarkdown feeds chunkers and LLMs directly with zero glue code.
Language coverage 80+ scripts Strong Latin coverage, pluggable OCRMultilingual scans of signage or receipts favor EasyOCR.
Non-document images Photos, screenshots, signs Built for page-shaped documentsWild imagery breaks page-layout assumptions.
EasyOCR: raw text, fast, multilingualDocling: structured documents, RAG-ready output

Five-question fit quiz

Answer for the corpus you actually need to ingest. Scoring weights tables and structure heavily because they dominate retrieval quality.

1. What are you mostly extracting from?

2. How much do tables matter to your answers?

3. What is your throughput and hardware situation?

4. What output does your chunker want?

5. How multilingual is the corpus?

EasyOCR
0
Docling
0

The pipeline you are choosing for

Every RAG ingestion run moves through four stages. Press play or click a stage to see where each engine earns its keep. Tools like Reseek collapse this whole assembly line into a single upload-and-query step; this diagram shows what happens under the hood when you build it yourself.

Scan Aa 12OCR Chunk Embed 300 dpi imagepixels to texttext to passagespassages to vectors

Or skip the assembly entirely

If you would rather not wire OCR models, chunkers, and vector stores together, hosted stacks like Reseek handle OCR extraction from images and PDFs with smart tagging and semantic search built in, so you upload documents and go straight to querying your content. Use the quiz above when you need to own the pipeline; use a managed layer when the pipeline is not your product.

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