Sign Language using Machine Learning
Machine learning based sign language recognition is the process of conversion of hand gestures into spoken language or text format. It efficiently aids in enhancing interaction with hard-of-hearing and deaf people. If you are unclear about your ML research work rest assured as we assist globally for all ML projects. All typed Paper writing services are supported by us globally. We provide necessary assistance for research, analysis and writing. Research paper editing are accompanied by us with proper explanation.
For recognizing sign language through the use of machine learning, we discuss step by step procedures to develop a framework:
- Description of Objective:
- Here we recognize usual words/phrases, one’s letters or both.
- Ensure whether we utilize video series data (for dynamic signs or phrases) or static images (for individual signs).
- Gathering of Data:
- Existing Datasets: Our project utilizes publicly available datasets like Lexicon Video Dataset, American Sign Language (ASL) or ASL Finger-spelling Dataset.
- Custom Data Collection: We also gather data with various factors (varied dimensions, colors, etc.) in several backgrounds, different light effects utilizing depth sensors or cameras.
- Preprocessing of Data:
- Video Preprocessing: When dealing with video data, our work segments them into a single sign series, retrieves frames and to capture movements, we utilize optical flow.
- Image Preprocessing: To improve generalization of our framework, we carry out various preprocessing steps like image resizing, normalization and augmentation like zoom, rotation, translation, etc.
- Feature Extraction:
- Conventional Techniques: We retrieve relevant features such as contours, Histogram of Oriented Gradients (HOG) or segmented skin color.
- Deep Learning: Convolutional Neural networks (CNNs) assist us to autonomously extract spatial characteristics from images and the integration of CNNs and RNNs/LSTMs or 3D CNNs helpful for series in videos.
- Model Chosen & Training:
- Static Signs:
- CNNs: We utilize and fine-tune the frameworks such as ResNet, VGG or MobileNet.
- Dynamic Signs or Sequences:
- CNN-LSTM: Our approach employs LSTM for temporal characteristics and CNN for spatial Characteristics.
- 3D CNNs: By analyzing video volumes, our networks examine the spatiotemporal features.
- Transformers: In video interpretation tasks, transformer frameworks help us to achieve better results.
- Training: We split the dataset into training data, validation data and testing data and augmentation techniques assist us to widen the training set.
- Evaluation:
- Metrics: To examine our framework, we consider several metrics like accuracy, recall, precision and F1-score. High recall and precision values are significant to appropriately understanding the signs.
- Visualization: We check where our framework making errors by visualize the forecasted signs compared with the actual outcome.
- Deployment:
- Implement our framework into an actual-time platform where the recording tools such as camera analyze the sign language and the model converts it into spoken language or text pattern.
- By using new emerging data and the individual’s reviews, we update and reconstruct our framework for possible enhancements.
Future Enhancements:
- Real-time Translation: For actual-time sign language conversion, implement our framework on edge based devices.
- Feedback Loop: We enable users to rectify the misunderstandings and for frequent enhancements, input this data into our framework.
- Expand Vocabulary: Our research intends to frequently consider enormous signs or adjust to various sign languages (for instance: ASL vs. BSL).
Limitations:
- Varied Interpretations: Based on person or area, we analyze some differentiations in sign language as same as the spoken language has some modulations.
- Ambiguity: In terms of various situations, some signs may appear identical but have varied meaning.
- Complexity of Movement: Sometimes, it is very difficult for us to capture and analyze complicated hand gestures.
We demonstrate that associating with the hard-of-hearing and deaf people and sign language professionals is approachable and their perception assists us to gather relevant data, validate our framework and actual-world implementation and also make sure about the importance and efficiency of our project.
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Sign Language Using Machine Learning Thesis Ideas
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- American Sign Language Recognition System Using Wearable Sensors and Machine Learning
Keywords:
American Sign Language, support vector machine, ensemble, confusion matrix, detection
American Sign Language used to help the people to increase the transmission between American Sign Language users and who don’t know the language. Sensors on both wrist are used to gather data and the gathered data were processed and trained by using Support vector machine and discriminant analysis classifier using a selective merging of features were discovered. Data gather from the wrists are trained using discriminant analysis classifier.
- Real-Time Sign Language Identification using KNN: A Machine Learning Approach
Keywords:
Sign Language, Machine Learning, Computer Vision, Image Augmentation, Image Processing, K-Nearest Neighbor (KNN)
A real time sign detector is a big development for hearing lessened and general public conversation. We have developed an accurate model that dependably classifies the majority of conditions including many languages. Our paper uses the OpenCV and ML method KNN. By taking a photo of sign language as input our method gives the output as audio or text.
- Smart Glove for Bi-lingual Sign Language Recognition using Machine Learning
Keywords:
Smart Glove, Arabic Sign Language, Flex sensors
To develop smart gloves to change the sign language into text or audio. To increase performance accuracy and decrease execution complexity our paper propose, implement and test non-visual based smart glove. To detect sign language by using five flex sensors and an accelerometer. ML classifiers like LR, SVM, MLP and RF are utilized to detect both American Sign Language and Arabic sign language. Random forest gives the better performance.
- Analyses of Machine Learning Techniques for Sign Language to Text conversion for Speech Impaired
Keywords:
Gesture control, Voice control, Alphabets Sign Language, Convolution Neural Network, Human Machine Interaction, Human Computer Interaction, Universal Serial Bus, Light Emitting Diodes
To find non-verbal communication our paper uses camera sensors. We initially create hand gesture recognition but most of them don’t know sign language then we introduce real time method for American Sign Language using deep neural network finger typing, reversed by a method with media pipe. To find the human gesture in images we use deep cognitive network. To construct this model we used computer vision, DL and ML. Media pipe model is a best one to detect multiple gestures.
- Recognition of Real-Time BISINDO Sign Language-to-Speech using Machine Learning Methods
Keywords:
Gesture Recognition, BISINDO, Speech
The sign language to speech system was enhanced to detect and change BISINDO sign language into speech by utilizing ML. The speech output will be easy to interact with other persons. Media pipe can be used for feature extraction. The support vector machine gives best outcome. Sign language to speech gives an result as audio speech without Internet connectivity. In real time the method able to detect both dynamic and static gesture.
- Sign Language Recognition using Machine Learning Algorithm
Keywords:
Sign-recognition, hand movements
The previous SLR style used palm-drafted to decide the change of sign language and make classification models. KNN has the tasks like curse of dimensionality, large dataset, class imbalance, noise and outliers. Our study uses CNN that remove the spatio-temporal elements automatically and remove without details known from unprocessed video and avoid design features.
- Development of a Sign Language Recognition System Using Machine Learning
Keywords:
Sign language recognition
Our paper proposes a real time sign language by using machine learning based system. Through a PC camera the hand detector module was used to recognize and take image of signer’s hand forming. CNN method was trained and compiled. CNN used in our paper include three convolutional layers and a SoftMax output layer the Adam optimizer for compiling and the categorical cross entropy loss function were used.
- A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute
Keywords:
deaf-mute person; hand gesture recognition; leap motion device; machine learning; multi-language processing; sign language dataset
Our paper uses ML to offer a full duplex communication method for deaf and mute (D-M). We offer an easy communication between non-deaf and mute (ND-M) and D-M without learn sign language. The proposed method change sign language to audio inevitably for ND-M. The N-M can interact by record their speech and convert them to text. This can improves hand gestures.
- Sign Language Prediction using Machine Learning Techniques: A Review
Keywords:
Media Pipe
American Sign Language also called hand sign language that is the communication between two members by the movement of both hands. The mostly used method for prediction is Convolutional Neural Networks (CNN) and K-nearest neighbors (KNN). We recognizes the hand sign language in our work.
- A Review of Segmentation and Recognition Techniques for Indian Sign Language using Machine Learning and Computer Vision
Keywords:
hand symbols, Segmentation, Recognition, Indian Sign Language
We used different methods for Indian Sign Language (ISL) detection and segmentation using machine learning and computer vision. The variation in sign language, lighting condition and background noise are tasks in ISL. We examine various strengths and problems of every method compared to accuracy, speed and robustness along different factors.
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