AI and ML-Based Offline Card Data Extraction & Beneficiary Verification

Context & Challenge

As part of the WASH digitalization project in the Rohingya camps, field teams use a mobile application to collect and verify beneficiary information. A key challenge was capturing beneficiary data from UNHCR-issued cards efficiently.

Manual entry of card information was time-consuming and vulnerable to human error, while barcode scanning alone was not sufficient for the required data-capture process. The challenge was further complicated by limited or unavailable internet connectivity in field locations, meaning the solution could not depend on continuous access to the central server.

AI/ML-Based Solution

3DEVs IT Ltd. addressed this challenge by integrating AI and Machine Learning-based image processing and data extraction directly into the offline-capable mobile application.

Instead of manually entering all card information, a field worker simply captures an image of the UNHCR card using the mobile device camera. The application processes the captured image locally and uses the AI/ML capability to recognize and extract the required beneficiary information.

The extracted information is converted into structured data and used to support beneficiary verification and field data collection. Since the processing works offline, field activities can continue even when the central server is temporarily inaccessible.

Workflow

UNHCR Card → Capture Image → Offline AI/ML Processing → Extract Required Data → Verify Beneficiary → Store Data Offline → Internet Restored → Synchronize with Central Server

Key Benefits

• Faster Data Collection: Reduces repetitive manual entry of beneficiary information.
• Improved Data Accuracy: Minimizes typing errors during field-level data collection.
• Offline AI Processing: Card information can be processed without continuous internet connectivity.
• Improved Field Productivity: Field workers can complete beneficiary verification more efficiently.
• Reliable Synchronization: Data collected offline is securely synchronized with the centralized platform once connectivity becomes available.
• Scalable Field Operation: The approach supports large-scale beneficiary data collection in connectivity-constrained humanitarian environments.

Outcome

The solution demonstrates a practical application of AI/ML within an offline-first mobile architecture. Rather than depending on cloud-based processing or manual data entry, intelligent card-image processing is brought directly to the point of data collection.

This enables faster, more accurate, and connectivity-independent beneficiary data capture and verification, while maintaining integration with the central WASH management platform through synchronization when internet access becomes available.

Part of the WASH Digitalization Initiative

This AI/ML implementation is part of the broader Digitalization of WASH Field Operations Management and Services Performance Tracking under the Rohingya Response project developed by 3DEVs IT Ltd.

The broader digital ecosystem integrates field operations, beneficiary services, facility monitoring, asset and inventory management, GIS-based monitoring, reporting, dashboards, and service-performance tracking into a unified platform.

View the Full WASH Digitalization Project →