From Image to Graph: Leveraging Tesseract OCR Engine for Document Chunking and GraphRAG
summary
Converts PDFs to images and runs Tesseract via pytesseract to extract word bounding boxes and confidences, then aggregates those words into a hierarchy of line/paragraph/block/page nodes with embeddings and writes them into Neo4j where Neo4j vector indexes power fast similarity search and Cypher walks that fetch surrounding context and linked metadata; the pipeline runs in seconds, stays on-premise for security, and already scales to 35,000+ documents from an acquired company enabling optimal chunk-level GraphRAG retrieval.
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chapters (6)
- 0:00Project introduction
- 0:43Challenge and use case
- 2:24Technology stack overview
- 4:53Implementation details
- 10:12Graph RAG methodology
- 13:00Project impact and conclusion
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