Document Processing Pipeline (OCR + NLP + Python + Elasticsearch + FastAPI) overview

A document ingestion and intelligence pipeline: OCR engines extract text from scanned PDFs and images, NLP models classify, extract entities, and summarize content, Python orchestrates the processing workflow, Elasticsearch indexes processed documents for full-text search and faceted retrieval, and FastAPI exposes the pipeline as a REST API for upstream applications. The ingest flow accepts documents via upload or S3 event triggers, runs OCR with Tesseract or cloud vision APIs, applies spaCy or Hugging Face transformers for NER, classification, and summarization, stores structured metadata in PostgreSQL, and indexes the full text in Elasticsearch. Celery or BullMQ handles async job processing for large batch ingestion. This stack powers legal document review, invoice processing, compliance document analysis, and enterprise search. The main tradeoffs are OCR accuracy on degraded documents and the compute cost of running transformer models at scale.