AI-Powered Multilingual OCR & Document Intelligence
Converts scanned PDFs and photographed forms into clean, structured Markdown across 10+ languages, including hard scripts like Urdu, Arabic and Amharic, with no manual keying.
Enterprises process thousands of documents monthly. Manual keying averages 6–8 minutes per page and scales linearly with volume, while standard OCR fails on non-Latin scripts and discards tables, headings and reading order.
Manual keying eliminated
6–8 min / page
Languages supported
Web process footprint
A three-layer pipeline (layout detection, then OCR, then Markdown assembly) behind an async REST API, with Tier-4 routing to Gemini 2.0 Flash when local models hit their accuracy ceiling.
Async five-stage pipeline behind a FastAPI 202-accept pattern keeps the web process lean (~300 MB RAM).
Two Celery queues isolate CPU-bound OCR (prefork) from I/O-bound Gemini calls (gevent).
Docling Heron collapses the DocLayNet taxonomy into 6 canonical labels for accurate structure segmentation.
Tier-4 routing: pages with ≥25% complex-script characters bypass local OCR for best-in-class accuracy.
REST API, 100 MB limit
langdetect + Tesseract OSD
Docling Heron (HuggingFace)
RapidOCR + Tesseract + TATR
GFM tables + XY-cut order
Eliminated expensive manual data-entry workflows at enterprise scale.
Unlocked searchable, machine-readable content from legacy multilingual archives.
Serves government, legal, research and healthcare digitisation use cases.
Why LLM features fail in production when the demo worked: messy real inputs, queues that starve each other, and failures that never raise an alarm.
A practical AI cost optimization guide from a real build: how task-based model routing cut one platform's AI spend by 99%, and trims most LLM bills 30 to 50%.
From messy source data to analytics-ready warehouses that cut cost. Let's scope it. I reply within one business day.