/portfolio

AI Parcel Monitoring and Tracking

Scope:

Operations optimization using image Recognition

Industry:

Logistics / Transportation

Customer:

Leading US logistics company (NDA)

Timeline:

6 month POC, 12 month for software development, integration and support

Team

Computer Vision Engineers

C++ Developers

UI Designer

Project Manager

Key technologies

Python

C++ | TensorFlow

Keras

OpenCV OCR with Tesseract | Darknet

YOLO

Problem

The process of parcel identification and sorting was costly, inefficient and relied on manual labour

GOAL

Improve efficiency, reduce human error, and scale the system to meet growing demand

SOLUTION

Precise detection and recognition of postal labels
Real-time image preprocessing and label data extraction
Automated sorting and parcel assignment for delivery vehicles
Integration with existing parcel tracking systems

Impact

Efficiency gain of 25-40%

Faster and more efficient parcel sorting and processing

Accuracy improved by 35-50%

Fewer sorting errors, even with damaged barcodes

Reduntant scalability

System handled 30% volume increase without bottlenecks

30% Process Cost Reduction

Automating document review processes by extracting key information from compliance documents