Learners will be able to implement real-time hand gesture recognition systems, apply OpenCV-based image processing techniques, develop robust hand segmentation logic, and automate browser actions using gesture-driven control. This course is designed to help learners progress from foundational computer vision concepts to a fully functional, end-to-end gesture-controlled application.
Implement Hand Gesture Recognition with OpenCV
Implement Hand Gesture Recognition with OpenCV
This course is part of Apply OpenCV for Real-Time Computer Vision Projects Specialization
Instructor: EDUCBA
Included with
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
6 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Build a real-time hand gesture recognition system using OpenCV and Python.
Apply image preprocessing, contour detection, and convex hull analysis for hand segmentation.
Integrate gesture recognition with browser automation for interactive control applications.
Details to know
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Recently updated!
February 2026
Assessments
11 assignments
Taught in English
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This course is part of the Apply OpenCV for Real-Time Computer Vision Projects Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
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There are 3 modules in this course
This module introduces learners to the overall hand gesture recognition project using OpenCV and establishes the foundational skills required to begin development. Learners set up the development environment, understand the project workflow, and implement core image processing techniques such as image loading, preprocessing, thresholding, and contour detection. By the end of this module, learners will be prepared to build reliable computer vision pipelines for real-time applications.
What's included
6 videos4 assignments
This module focuses on the core computer vision logic required for accurate hand detection and segmentation. Learners develop background modeling techniques, implement multi-stage hand segmentation functions, and refine results using contour analysis, convex hulls, and defect detection. The module emphasizes accuracy, consistency, and robustness in real-time gesture recognition systems.
What's included
11 videos4 assignments
This module integrates all previously developed components into a complete, executable system. Learners manage function execution flow, validate gestures, and map recognized gestures to browser automation actions. The module concludes with demonstrating the final working system and validating end-to-end functionality in a real-world use case.
What's included
6 videos3 assignments
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