One of the essential applications of machine learning is “Sign language classification”. This connects the communication gap for the hearing-impaired community. A well-crafted dissertation will handover for your sign language classification we assure high quality, customized written research work in machine learning topics that meets up to your requirements. Latest techniques by merging various algorithms we derive the result that we have expected. By making frequent communication with the scholars, we solve all your doubts in your research work as and when needed. The project includes the process of, training a machine learning model, detecting and categorize the hand symbols responds to various signs in a sign language.
Here, we provide the step for executing this project:
The primary aim of this project is a machine learning model is created by us that categorize various hand symbols of a sign language perfectly.
The execution of sign language classification system that is attained successfully and we deeply observe the great impacts which enables the smooth communication for the hearing-impaired and improve the equality. By online platform more than 150+ countries we guide machine learning projects. We assure you that our research proposal work will meet up to your standards. Drop a message for more assistance.
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One-Shot Learning, Image Classification, Sign Language Recognition, Siamese Network
Our goal is to change sign language (SL) to text and give an interactive link between deaf-mute communities and the hearing population. The American SL is hard to convert this method to other sign language so our paper proposed one-shot learning a novel ML method that permits the model to study and recognize by utilizing limited data for training, and we have to find and classify the hand signs and change them to equivalent text.
sign language classification, spatial-temporal graph convolutional network, human body joints, attention mechanism, cockpit sign language
We propose an adaptive graph convolutional network based on skeleton features (NA-AGCN) that is the path to increase the ST-GCN method. We can first add the light weight normalized attention module NAM is added, that can increase the network efficiency and an adaptive graph convolution module is suggested to break the original graph and the convolutional adjacency matrix can divide the matrix into three subgraphs to realize the extraction of features and to improve the flexibility of the graph.
Indian sign language, wearable sensors, multistage classification, random forest, extreme gradient boosting
An ensemble ML model with multi stage classification of signs has been proposed in our paper. First we classify the sign as static or dynamic by utilizing a binary classifier and then the sign is trained to classify any of the two methods. RF and Extreme Gradient Boosting machine have been compared to classify the signs from two categories. Our proposed multistage classification achieves the high accuracy rate.
Machine learning (ML), decision tree (DT), Deaf-dumb hearing impaired
The Arabic sign language (ArSL) can deal with Iraqi sign dialect and the goal of our paper is to deal with Iraqi data dictionary Utilized in deaf schools. Our study uses the ML based Decision tree method is the best method that can recognize the voice especially in Arabic language to classify the spoken Arabic language to SL image to get a accurate best result. We have to contrast this with two studies by utilizing CNN and RNN methods.
Convolutional Neural Networks, Transfer learning, K-Means clustering.
We suggested a CNN method to recognise the Indian SL static character. To compare our methods with various feature extraction techniques that can be tested on CNN method in our paper. The CNN model can be utilized to train our dataset and the model’s feasibility can be noticed as possible. Our proposed method gives the best accuracy.
Pattern Recognition, Data Processing, Optimizer Algorithm, Artificial Intelligence
The sign language classification method can be based on the recurrent neural network but that has the issue of complex model input data preprocessing, network model and slow training speed. So our paper uses a classification method based on recurrent neural network bidirectional long term and short term memory network and that go forward an optimized scheme from network data processing, network structure and network training model.
7.Intelligent real-time Arabic sign language classification using attention-based inception and BiLSTM
Bio-inspired computing, Deep learning, Sign language, Real-time classification, Inception, BiLSTM
Our paper suggested a method based on CNN Inception model that utilizes an attention mechanism for retrieving spatial features and Bi-LSTM for temporal feature extraction. Our suggested method can be tested on dataset with increased variable features namely clothing, variable lighting and variable distance from camera. Real time classification achieves significant early detection when compared to offline tasks.
Continuous sign language recognition, Generative Adversarial Networks, Sign classification, Feature dimensionality reduction, Hyperparameter optimization
Our paper proposes a hyperparameter based optimized Generative adversarial networks (H-GANs) to classify the sign gestures in three phases. At first stage we used stacked variational auto-encoders (SVAE) and PCA to reduce feature dimension then in second stage the H-GANs employed Deep LSTM as generator and 3D CNN as discriminator. Then the third stage uses DRL for hyper parameter optimization and regularization. By receiving incentive points the PPO optimises hyperparameter and the BO regularises the hyper parameter.
Bangla sign language; alphabets and numerals; semantic segmentation
Our study uses Deep ML methods for accurate and reliable BDSL alphabets and numerals by utilizing two datasets. We compared classification with and without background images to decide the best model for BDSL numerals and alphabets. The CNN method has been trained with background images and that will give better accuracy than without background.
Event camera, spiking neural network, DVS-sign language, intelligent system
We first chose an event-based sign language gesture dataset that have two sources: traditional sign language videos to event stream (DVS_Sign_v2e) and DAVIS346 (DVS_Sign). In current dataset the data can be classified into five classification, verbs, quantifiers, position things and people familiar to actual situations and robots provide instructions or assistance.
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