OCR & Document IntelligenceProduction Deployed

KWC OCR — Handwritten Table & Clinical Extraction

Deep Tabular Extraction from Legacy Medical Records — Computer vision and OCR pipeline parsing irregular cell boundaries, de-skewing scanned pages, and transforming handwritten tabular clinical logs into structured records.

PythonOpenCVTable ExtractionHandwriting RecognitionDocument AIVision-Language Models (Gemini 3.1 Pro)

Handwritten Table OCR

Tabular Extraction

DateWound (cm)Exudate

10/122.4 x 1.2Serous

10/191.8 x 0.9None

Legacy Records UnlockedClean Structured Data

Measured Production Impact

High

Extraction Recall

Dense tabular notes

Legacy

Chart Unlocked

Handwritten records

Core

Foundation

Enterprise OCR layer

Structured

Digital Matrix

SQL/JSON export

The Legacy Data Bottleneck

Historic clinical charts often contain critical tabular logs (wound measurements, vitals trends, dosage histories) scribbled by hand in irregular paper grids that standard OCR tools fail to parse.

Deep Vision & Grid Parsing

Engineered computer vision algorithms for cell boundary detection, rotational de-skewing, and handwriting character recognition, converting unstructured paper scans into structured digital records.

Enterprise Foundation

Established a robust, reusable document intelligence pipeline that became the technical bedrock for downstream EMR ingestion and multimodal clinical analytics.

Key Engineering & Operational Highlights

Handwritten Table Extraction: Automatically unlocked trapped tabular clinical data from handwritten charts into structured digital records.

Reusable Architecture: Established the enterprise OCR foundation leveraged across subsequent document digitization initiatives.

Boundary & Cell Normalization: Corrected skew, resolved irregular grid lines, and extracted handwritten clinical notations reliably.

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