Case Study: Digital Divide Data achieves 99.9% annotation accuracy and faster automotive AI model development with V7

Scaling Leading Automotive AI Model Development with End-to-End Data Annotation

Digital Divide Data partnered with V7 to help an automotive AI team build high-quality training data for vehicle recognition and OCR number plate reading. The customer needed a global dataset covering many national vehicle makes and license plate formats, while maintaining more than 99.9% accuracy and keeping up with rapid model development demands.

Using V7 Darwin and Digital Divide Data’s dedicated annotation team, the project streamlined labeling, review, and dataset management for up to 200,000 images per week. V7 and Digital Divide Data helped ramp throughput from 35,000 images a week to around 200,000, reduced time spent on dataset review, and delivered training data with over 99.9% accuracy.

Digital Divide Data

Sameer Raina

Chief Executive Officer