Mar 19, 2023 · In this blog, we will explore how to detect the alphabet associated with the hand sign using Hand landmark model (using MediaPipe), Random Forest Classifier and Intel oneAPI-optimized...

Jan 14, 2016 · Sign language recognition (SLR) has been widely used for communication amongst the hearing-impaired and non-verbal community. This paper proposes an accurate and robust SLR framework using an improved decision tree as the base classifier of random forests.

Nov 12, 2015 · Sign language recognition (SLR) has been widely used for communication amongst the hearing-impaired and non-verbal community. This paper proposes an accurate and robust SLR framework using an improved decision tree as the base classifier of random forests.

This research explores the application of the Random Forest algorithm for recognizing sign language gestures. By utilizing input images representing various sign gestures, the Random Forest Classifier (RFC) categorizes these gestures into predefined classes, ranging from A to Z.

Apr 4, 2024 · The proposed method, called Lightweight Attentive VGG16 with Random Forest (LAVRF), combines a streamlined variant of the VGG16 model with an attention mechanism and employs a Random Forest classifier for sign language recognition.

The proposed idea is to develop a pair of sensor gloves which detects Indian Sign Language (ISL) gestures and converts them into audible speech. The gloves are mounted with various sensors and modules such as the Arduino Nano microcontrollers, flex sensors, touch sensors, Inertial Measurement Units (IMU), RF and Bluetooth modules.

Jul 9, 2021 · In this article, the way to create a secure system using face recognition and sign language as an authentication method is discussed and an application using ESP32-CAM is developed and tested.

Choose between SVM and Random Forest by modifying the classifier in train_classifier.py. Update the labels_dict in inference_classifier.py accordingly. This project focuses on real-time sign language detection using computer vision and machine learning techniques.

In ASLR common algorithms such as PCA, Random Forest Classification (RFC), Deep Neural Network (DNN), CNN, Data Augmentation, Manifold Learning, KNN, Gaussian Naïve Bayes (GNB), SVM, and Stochastic Gradient Descent (SGD) were used. And observe their accuracy, error, loss, and other key values, organize an objective experimental report.

The Sign Language Gesture Recognition project utilizes Python, OpenCV, and the powerful Random Forest algorithm. It captures and preprocesses video frames, extracting hand landmarks using Mediapipe. These landmarks are then used as input for the an small nerual network, trained on a labeled dataset of sign language gestures.