Hand Gesture Recognition via sEMG signals with CNNs (Electrical and Computer Engineering - MSc Thesis)

[Neurocomputing 2019] Fast and Robust Dynamic Hand Gesture Recognition via Key Frames Extraction and Feature Fusion

Hand Gesture Recognition using python.

Hand Gesture Recognition Code for Matlab

Recognises hand gestures with OpenCv and Numpy

This project aims to design a real-time vision-based hand gesture recognition system with machine learning techniques, which potentially makes deaf-and-mute people life easier.

Hand Gesture Recognition with Android

Image processing code for hand gesture recognition using OpenCV

Based on skin color detection using otsu

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This project is based on eaglesky's HandGestureApp

hand gesture recognition using HSV from Videos & Images

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Hand gesture recogniiton algorithm on a resource-limited interactive wristband. This HGR algorithm is based on matching axis-crossing codes.

Real-time hand gesture classifier using MediaPipe and custom CNN. Features hand tracking, wireframe visualization, and gesture recognition. Includes data collection mode for training new gestures and interactive visualization of classification confidence.

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Hand gesture recognition using convolutional neural network and recurrent neural network

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HandGestureRecognition

Implementing hand tracking using opencv

Python script that uses OpenCV2 to recognize how many fingers the user is showing on a camera.

Sign language is a mainstream method of communication by people suffering from hearing and speech impairment and other disabilities. Around 360 million people globally are suffering from disabled hearing loss. Minimizing the communication gap between differently-abled and normal people becomes a necessity to ensure effective communication among all. In this paper, a method for developing a system which recognizes cue symbols of sign languages to act as a communication medium between healthy and differently-abled people has been proposed. Two standard sign languages are considered for reference, as, cue symbols vary for each region. The system includes a hand gesture recognition system, where gestures are given as input and the corresponding output is in the form of text as well as speech. We have used gradient value for splitting two or more gestures from a continuous video sequence and extracting key frames. Features of pre-processed key frames are extracted using OH (Orientation Histogram) and dimensions of these features are reduced using PCA (Principal Component Analysis). SVM (Support Vector machine) classifier is used for classification of the isolated pre-processed gestures. After the classification process, corresponding connotation of gestures given as input is obtained

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Used test image to predict the probability of the hand gesture in the image and used tensorflow for computational graph

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Using color- based masking and CNN

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Vision and Point Cloud Analysis of Hand Gestures