Introduction This tutorial shows how to use OpenCV template matching in Python to find a smaller image inside a larger image.We will solve a fun Where’s Waldo style problem using a single, simple algorithm.The code reads two images, converts the scene to grayscale, and runs cv2.matchTemplate to measure visual similarity.We then extract the best match

Introduction In this tutorial, you will build a voice-controlled, real-time object detection app in Python using OpenCV DNN and YOLOv4-tiny, powered by Speech Recognition for speech-to-text. The goal is to let you speak the name of an object and instantly highlight matching detections on the live camera feed.This approach blends real time object detection python

Text Recognition in Images with OpenCV and EasyOCR This Python script demonstrates how to perform text recognition in image files using the combined power of OpenCV, EasyOCR, and Matplotlib. The process begins by loading an image with text recognition OpenCV techniques, then detecting and extracting text using EasyOCR’s pre-trained English model. The detected text is

Introduction This tutorial shows how to build an OpenCV image comparison pipeline in Python that detects visual differences between two images and highlights them with bounding boxes.You will learn how to preprocess the input, isolate the two pictures inside a collage, align their sizes, compute pixel-wise differences, reduce noise, and extract clean contours of the

Introduction In this tutorial you will build a fast and lightweight car detection pipeline in Python using OpenCV background subtraction.The approach uses the MOG2 model to separate moving vehicles from a static scene, making it ideal for traffic videos and fixed cameras. You will generate a clean foreground mask, refine it with morphology, and detect

Introduction This tutorial shows how to perform OpenCV word segmentation in Python to prepare scanned pages for OCR.You will learn to isolate text with thresholding, smooth noise with Gaussian blur, merge characters into words using dilation, and detect text regions with contours. We first segment lines, then segment words within each line, and finally draw