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- W3033038454 abstract "Sign language is a language that uses body movements, gestures, and facial expressions to conveymessages or information. Sign languages are commonly used among hearing-impaired as well asmutism community. However, static gesture recognition system is not capable of recognizingtemporal content of gesture that contains movement. The main objective of this project is todevelop a vision based American Sign Language (ASL) recognition system that has the capabilityto recognize the ASL that contains temporal feature. American Sign Language Lexicon VideoDataset (ASLLVD) is used in this project. Out of 3300 signs in the dataset, only 5 gestures inused during the neural network and Support Vector Machine (SVM) video sequence trainingprocess. Various image processing techniques such as GrabCut background subtraction,grayscaling, and image resizing operation are performed on the video dataset. In order to mitigatethe temporal and spatial complexity, mean structural similarity index (MMSI) technique is usedto reduce the temporal length of the video dataset while reserving the video portion that has themost information. Feature extraction of this dataset is done using multiple Convolutional NeuralNetwork (CNN) models for instance InceptionResnetV2, VGG19, DenseNet201 andMobiliNetV2. Among these CNN models, the best validation accuracy of 15.24% is obtained byInceptionResnetV2 model. In summary, the model is too overfit due to small dataset size. Tosolve the overfitting issue, SVM is also used alongside Histogram of Oriented Gradient (HOG)descriptor. The best accuracy obtained from SVM method is 39.12%." @default.
- W3033038454 created "2020-06-12" @default.
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- W3033038454 date "2020-01-01" @default.
- W3033038454 modified "2023-09-24" @default.
- W3033038454 title "Computer Vision Based Sign Language Recognition System" @default.
- W3033038454 hasPublicationYear "2020" @default.
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