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Tag: computer vision

Explore the cutting edge of computer vision, where machines interpret images and videos to drive innovation. We cover core techniques like convolutional neural networks (CNNs), transfer learning, and real‑time video analytics using OpenCV, TensorFlow, and PyTorch. See how industries apply object tracking, facial recognition, and quality inspection to improve safety and automation. With hands‑on tutorials, model optimization tips, and ethical considerations, you’ll gain the skills to build robust vision systems. Bring vision to your projects—dive into our Computer Vision tutorials today!

  • Building OCR & Detection Systems with Deep Learning

    Building OCR & Detection Systems with Deep Learning

    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.