📃 Automating Insurance Document Processing with OCR and NLP

Hey Dev Community! 👋

I’m Manognya Lokesh Reddy, an AI grad student and ML engineer. In this post, I’ll share a project I worked on during my Co-op at Donyati India Pvt Ltd, where we built an intelligent system to digitize and extract information from handwritten insurance documents using OCR, NLP, and Computer Vision.

🧩 The Problem
In the insurance domain, processing scanned or handwritten documents is a manual, time-consuming task. It often involves:

Human data entry (error-prone and expensive)

Difficulty in reading handwritten or low-quality text

No structured digital storage for later retrieval

So we built a document processing system that:

Uses OCR to read handwritten text

Applies NLP to structure the extracted data

Delivers clean, searchable digital records

⚙️ Tech Stack
Python

Tesseract OCR + OpenCV – for text extraction

NLTK / spaCy – for text preprocessing and structuring

Custom pipelines – for automation and error handling

Flask – to prototype internal APIs

🏗️ What We Built
🔍 Step 1: Image Preprocessing
Used OpenCV for:

Denoising

Thresholding

Skew correction

Enhanced image clarity for better OCR results

🔡 Step 2: OCR Extraction
Used Tesseract to extract both printed and handwritten content

Achieved up to 97% accuracy with tuned configurations and training on custom handwriting samples

🧠 Step 3: NLP Pipeline
Cleaned text with:

Lowercasing

Removing stopwords

Entity recognition

Parsed key fields like:

Policyholder name

Claim amount

Date of accident

Medical report status

🔄 Step 4: Structuring & Output
Exported extracted fields into:

JSON for APIs

CSV for dashboard ingestion

Built a lightweight dashboard for visualization (internal use)

📊 Results
📌 97% OCR accuracy for handwritten documents

⏱️ Reduced document processing time by 30%

📁 Enabled structured, searchable storage for thousands of forms

📉 Minimized manual errors and improved regulatory compliance

💡 What I Learned
OCR needs custom preprocessing to be truly accurate—raw images won’t cut it

Handwriting OCR is still challenging, but small tweaks in image prep + model configs go a long way

NLP helps bring structure to chaotic data—without it, you're just digitizing noise

This kind of automation can save millions in ops costs in large enterprises

🌍 Applications Beyond Insurance
Legal document scanning

Bank KYC automation

Hospital record digitization

HR form processing in enterprises

🚀 What’s Next?
Adding language translation to support multilingual document digitization

Integrating signature verification for fraud prevention

Exploring LLM-powered extraction with LangChain + GPT for deeper semantic parsing