Automating Government Form Filling With Intelligent Document Processing Solutions

Summary

Visa and government-related processes demand repetitive form-filling based on scanned documents such as passports and visas. &v-highlight;Traditional OCR tools&:v-highlight; extracted text but failed to reliably map that data into form fields, resulting in errors, inefficiencies, and compliance risks. Mechsoft delivered a secure, &v-highlight;AI-powered solution&:v-highlight; combining OCR with an LLM (&link-https://www.ibm.com/think/topics/large-language-models/;Large Language Model&:link-https://www.ibm.com/think/topics/large-language-models/;) that &v-highlight;intelligently extracted, interpreted, and mapped document&:v-highlight; data directly into government forms—while maintaining privacy through local deployment.

Overview

Organizations in regulated sectors like immigration and public service deal with massive volumes of document-driven workflows. The &v-highlight;traditional OCR-based approach&:v-highlight; to document processing fails to address semantic understanding, multi-line fields, or trust in automation. It creates need for intelligent systems that combine &v-highlight;AI-based OCR&:v-highlight; with contextual understanding and form automation.

Business challenge

In visa processing workflows, HR or operations teams often need to extract key data from multiple scanned documents (passports, visas, medical cards, etc.) and enter that data into standardized government forms.

&v-highlight;Key pain points: &:v-highlight;

• Existing OCR tools like Tesseract often &v-highlight;misplaced or misinterpreted data.&:v-highlight;
• Multi-line names or address fields got truncated or placed into wrong form fields.
• No visibility into low-confidence predictions force &v-highlight; full manual review.&:v-highlight;
• Cloud-based AI tools posed a &v-highlight; data security and compliance risk.&:v-highlight;

&v-highlight; The client wanted:&:v-highlight;

• &v-highlight; Accurate document data extraction&:v-highlight; and field mapping
• Highlighting of low-confidence fields for validation
• &v-highlight; Secure, on-premise deployment&:v-highlight; without relying on external APIs
• &v-highlight; Seamless integration&:v-highlight; with their existing &link-/products/visa-management-system;Visa Management System&:link-/products/visa-management-system;

Solution approach

Mechsoft engineered an AI-powered Document-to-Form Module that combines traditional OCR with a custom-built LLM-based AI mapping engine.

&v-highlight;System Architecture & Workflow&:v-highlight;

1. OCR Layer

• Extracted raw text from scanned documents using OCR (Tesseract + extensions).

2. LLM-Based Mapping Engine

• Used a lightweight, on-premises LLM trained on thousands of document samples.
• Automatically identified and matched extracted data to the correct form fields, even for complex fields like full name, address, etc.

3. Confidence Scoring & QC

• Each mapped field was assigned a confidence score.
• Fields below a configurable confidence threshold were flagged for manual review.

4. Deployment & Integration

• Local deployment ensured data security and compliance.
• Directly integrated into the client's Visa Management System to reduce user workload.

5. Outputs

• Predicted visa renewal counts by month and group.
• Confidence intervals and influencing factor summaries.
• Alerts for HR teams when predictions deviated from actual outcomes.

6. Results/Benefits

Key learnings

• Traditional OCR is insufficient for semantic data mapping; LLMs add essential context.
• Local deployment and enterprise-grade security options are vital for adoption in regulated industries.
• A human-in-the-loop approach enhances trust and ensures real-world reliability.