Document Layout Intelligence & Neural OCR
Bilingual document understanding, layout classification, and deterministic reading order reconstruction
Problem Context & System Scope
An enterprise document intelligence system engineered as the foundation for modern academic and administrative workflows.
Upgrades basic OCR text extraction into a semantic document understanding pipeline capable of classifying regions (titles, questions, tables, figures, equations), reconstructing deterministic multi-column reading order, and extracting structured HTML tables.
Runs entirely on local CPU with zero external API dependencies, guaranteeing privacy and data ownership.
Technical Breakdown & Pipeline Engineering
Bilingual Neural Recognition Pipeline
Dual recognition backbones tuned for mixed-script text.
- Accurate character and word recognition across English and Bengali scripts.
- Handles rotated, sheared, and low-contrast scanned documents.
Layout Classification & Reading Order
PicoDet-LCNet region classification with topological sequencing.
- Classifies 11 semantic classes: title, question, heading, paragraph, table, figure, equation, header, footer, etc.
- Reconstructs natural reading orders across single, double, and complex multi-column exam formats.
- SLANet extracts cell structures and converts tables into clean HTML representations.
Engineering Hardships & Resolution
Implemented bounding box geometric cluster analysis that segregates vertical gutters before line sorting.
Quantitative Results & Impact
- Delivered a production-ready document intelligence microservice with interactive Next.js split-screen verification.
- Zero cloud API latency, 100% private on-premise execution.