Clinical OCR Pipeline

Extract characters and layouts with clinical context. Layout-agnostic OCR neural networks trained specifically on medical charts.

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Data Pipeline Workflow

Our layout engine maps bounding coordinates before converting character arrays, keeping text columns and grids structurally connected.

Layout Mapping

Detect document structure, tables, and bounding boxes using layout-analysis neural networks.

Segment OCR

Parse text characters within individual segments, handling skewed handwriting and faint ink.

Coordinate Align

Map text coordinates to coordinate grids to preserve clinical context (e.g. connecting lab names with lab results).

Layout bounding telemetry

HIPAA Secure Node

OCR Segmentation coordinates

# Bounding box 1 [x: 120, y: 350, w: 200, h: 40]

PATIENT NAME: JANE DOE

# Bounding box 2 [x: 120, y: 400, w: 180, h: 40]

DOB: 11/12/1984

Developer Specifications

API Integration & Data Specifications

Integrate this platform module with standard code requests. Receive clean, validated, and normalized payloads that fit into EHR databases or server hooks.

ocr-coordinates.json

{
  "documentId": "doc_8f17a942b083",
  "ocrEngine": "clinical-layout-v4",
  "tokens": [
    { "text": "JANE", "boundingBox": [120, 350, 60, 20], "confidence": 0.99 },
    { "text": "DOE", "boundingBox": [190, 350, 50, 20], "confidence": 0.99 },
    { "text": "11/12/1984", "boundingBox": [120, 400, 110, 20], "confidence": 0.98 }
  ]
}

Key Outcomes & Benefits

Layout Agnostic

No templates required. Reads varying clinic referral designs without code changes.

Table Structuring

Correctly identifies and aligns complex multi-column laboratory panel tables.

Error Flagging

Automatically highlights illegible characters or smudges for clinical review.

Experience the MedIngest Platform

Request a custom data extraction assessment. Our healthcare integration architects will evaluate your email attachments, faxes, scanned files, and EHR configurations.

Schedule Assessment

{ "documentId": "doc_8f17a942b083", "ocrEngine": "clinical-layout-v4", "tokens": [ { "text": "JANE", "boundingBox": [120, 350, 60, 20], "confidence": 0.99 }, { "text": "DOE", "boundingBox": [190, 350, 50, 20], "confidence": 0.99 }, { "text": "11/12/1984", "boundingBox": [120, 400, 110, 20], "confidence": 0.98 } ] }