Computer vision is revolutionizing industries by enabling machines to see and interpret the world. From OCR to real-time detection, AI-driven vision systems enhance security, automation, and efficiency.

OCR (Optical Character Recognition) converts scanned images or PDFs into readable text. With libraries like Tesseract or deep learning models (CRNNs), you can extract structured data from invoices, forms, or IDs.

Detection systems, using YOLO or SSD architectures, identify objects like people, cars, or tools in real-time video feeds. Retail stores use them for footfall analysis; factories for safety monitoring; banks for facial verification.

Building a vision system involves:

Collecting and annotating data

Training a model using TensorFlow or PyTorch

Optimizing it for edge deployment (e.g., Jetson Nano)

Deploying with Flask or FastAPI APIs

A real-world example is a parking solution that detects vacant spots via CCTV feeds, sends alerts, and optimizes flow.

Computer vision adds intelligence to cameras, turning raw footage into actionable data. Its applications are growing—from agriculture to eKYC—and the results are impressive.