The challenge
Large archives of scanned documents had no machine-readable text, so they could not be searched, embedded or used by an AI assistant.
Raw OCR output is noisy and unstructured, which makes it unsafe to feed straight into a retrieval system.
- 01
PDFScanned document intake
- 02
ImagePage-to-image conversion
- 03
CleanPreprocessing for OCR accuracy
- 04
OCRText extraction
- 05
StructureVector-ready JSON
Architecture · 5 stages
- 01
PDF
Scanned document intake
- 02
Image
Page-to-image conversion
- 03
Clean
Preprocessing for OCR accuracy
- 04
OCR
Text extraction
- 05
Structure
Vector-ready JSON
The solution
I built a five-stage pipeline: PDF intake, image conversion, image preprocessing, OCR and structuring. It cleans and normalizes each page and emits consistent JSON ready for embedding and vector-store ingestion.
Downstream RAG systems then retrieve from accurate text instead of OCR noise.
Engineering highlights
- Five clearly separated, testable stages
- Image preprocessing to raise OCR quality
- Consistent JSON schema for downstream systems
- Drop-in feed for vector databases and RAG
Technology
- Python
- OCR
- PDF processing
- Vector embeddings
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