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Tawanda21/Localization-ML-AI-Driven-Document-Processing-for-African-FinTech-LegalTech

Domain:

digital infrastructure

Record type:

software
Creator:
Taw
Host:
AI-powered document processing platform designed for African businesses. Extracts structured data from unstructured documents (invoices, contracts, IDs) Combines OCR + NLP + LLMs for document understanding Handles African-specific document formats and languages Deployed as a FastAPI microservice with Docker. # Localization ML: AI-Driven Document Processing for African FinTech/LegalTech **An Intelligent Document Processing (IDP) tool I built specifically for the African market.** ## What I Built I noticed a real problem while working with African FinTech and LegalTech startups: **local documents are a mess.** One Omang ID from Botswana looks completely different from a Kenyan national ID. Utility bills have no standard format. Bank statements mix English with local languages and random abbreviations. Off-the-shelf OCR tools like Tesseract alone just don't cut it. So I built **Localization ML** - an Intelligent Document Processing pipeline that extracts structured data from these messy, real-world documents. It uses **Computer Vision** to understand document layouts and **NLP** to pull out specific entities (names, ID numbers, dates, amounts). Feed it a scan or photo, and it returns clean JSON ready for KYC, loan approvals, or legal compliance. ## What It Actually Does - **OCR + Layout Understanding** - I paired Tesseract 5 with Microsoft's **LayoutLMv3** so the system understands *where* things are on a page, not just *what* text exists. - **Custom NER for Local Entities** - I trained a Named Entity Recognition pipeline to spot: - `PERSON` (full names, including multiple first/last name conventions) - `DATE` (issue dates, expiry dates - handles DD/MM/YYYY and local formats) - `ID_NUMBER` (Omang, passport, voter IDs with country-specific validation) - `AMOUNT` (transaction values in BWP, KES, NGN, ZAR - with and without commas) - `ADDRESS` (messy, multiline local address formats) - **Docker-First Deployment** - I containerized the entire thing. No "works on my machine." You run one command and it's live. - **REST API** - A simple `/extract` endpoint. Drop in an image or PDF, get JSON out. - **Localization Ready** - I designed it so you can fine-tune for new African document types without rebuilding everything. ## How I Built It (Tech Stack) | Component | What …

Visit

github.com

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