End-to-End Document Intelligence Pipeline

Thousands of medical and insurance documents arrived as poor-quality scans mixing printed forms, handwriting, checkboxes, and multi-page records. No off-the-shelf OCR could handle the full journey from noisy image to validated, structured data.

Each failure mode (blur, shadows, handwriting variety, OCR typos, inconsistent formats, cross-page conflicts) needed a dedicated solution. The client needed all stages working together as one reliable production system, not a collection of disconnected scripts.

I designed the platform as eight specialized stages: a CNN noise classifier routes each scan to the right U-Net denoising pipeline; DBNET/EAST models localize handwriting and a MobileNetV3 detector finds handcheck marks; CRNN and trOCR models recognize printed and handwritten text; Beam Search with language models fixes OCR errors; a codification engine normalizes dates, measurements, and domain codes; and an interpage correlation system cross-references entities and flags conflicts for review.

Every stage is independently deployable, so each one can be retrained, scaled, and improved without touching the rest of the pipeline.

All eight stages run as containerized services on AWS Kubernetes with Jenkins and GitHub Actions CI/CD. Each stage has its own deployment pipeline, so model updates roll out independently with zero-downtime rolling deploys.

Kubernetes auto-scaling handles daily document volume spikes. Per-stage health checks and CloudWatch monitoring catch failures before they cascade downstream. The modular design later allowed an LLM extraction layer to slot on top of the same infrastructure, raising accuracy to 88%.

Reached 84% end-to-end extraction accuracy in production, replacing hours of manual extraction per document batch.

The platform processes high document volumes daily on AWS Kubernetes; its modular design allowed the LLM-based extraction layer to slot in on top and raise accuracy to 88%.