[PDF] Top 20 Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks

Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks

... the recurrent activations are propagated back to the extra layer—called the context layer—which cop- ies the activation pattern from the output layer on the last ...adding recurrent connections at different ... See full document

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Comprehensive Performance Study of Existing Techniques in Hand Gesture Recognition System for Sign Languages

... Gesture Recognition System (HGRS) has been proved to be a powerful communication tool for deaf and dumb users, irrespective of geographical ...various sign languages (denoted by hands mostly) globally along ... See full document

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Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition

... world, Arabic sign language (ArSL) has received little attention in sign language recognition ...on ArSL. Signer- independent recognition of Arabic ... See full document

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Enhancing The Recognition Of Arabic Sign Language By Using Deep Learning And Leap Motion Controller

... different recognition techniques; geometric template matching, artificial neural network and cross correlation to classify 26 alphabet gestures of American sign language, they reported that ... See full document

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Arabic Diacritization with Recurrent Neural Networks

... in Arabic orthography presents a problem for many language processing tasks, in- cluding acoustic modeling for speech recognition, language modeling, text-to-speech, and morpho- logical ...ral ... See full document

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Tuning Recurrent Neural Networks for Recognizing Handwritten Arabic Words

... Artificial neural networks have the abilities to learn by example and are capable of solving problems that are hard to solve using ordinary rule-based ...the neural network size is a hard task ... See full document

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Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

... procedure using a set of input-output pairs, the training ...to neural networks [20]. Adaptive networks have no synaptic weights, instead they have adaptive and nonadaptive ...a neural ... See full document

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Arabic Sign Language Recognition through Deep Neural Networks Fine-Tuning

... of sign language recognition, thus reducing the need for human intervention during the ...recognize sign language through feeding a convolutional neural network (CNN) with images ... See full document

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Generating Image Captions in Arabic using Root Word Based Recurrent Neural Networks and Deep Neural Networks

... natural language processing ...generating Arabic descriptions of an image is extremely ...like Arabic are heavily influenced by ...of Arabic to generate captions of an image directly in ... See full document

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HAND POSES DETECTION USING COVOLUTIONAL NEURAL NETWORK

... gesture recognition, calculation and text output and the achieved ...gesture recognition based on input-output hidden markov models and diverse aspects of gestures are discussed in this process where ... See full document

5

Recognition of sign language using neural networks

... trained using backpropagation until it achieves its maximum level of ...Several recurrent units are then added to the hidden layer, which are fully connected to the input layer, and have recurrent ... See full document

219

Continuous Arabic Sign Language Recognition in User Dependent Mode

... Since sign language varies in both spatial and temporal domains, the extracted feature vectors are sequential in nature and hence simple classifiers might not ...vectors using a suitable operation ... See full document

9

Sign Language to Number by Neural Network

... propagation neural network use to classify sign language number symbol according to fisher score ...propagation neural network is created by generalizing the gradient descent with momentum ... See full document

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Human emotion recognition in video using subtraction pre-processing

... This section presents the details about the whole emotions recognition system. Figure.3 shows the sketch of the whole system that includes pre-processing and test progress. The video dataset of RAVDESS [11, 12] is ... See full document

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Convolutional Neural Network Language Models

... To analyze whether all this information was effectively used, we took our best model, the CNN+MLPConv+COM model with embedding size of 256 (fifth line of second block in Table 1), and we identified the weights in the ... See full document

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Comparison of Neural Network Parameters for Classification of Arabic Handwritten Isolated Characters

... a recognition rate of 99%. Their system combines neural networks and ...isolated Arabic manuscript characters according to whether or not they contain diacritic ...A neural network is ... See full document

8

Language Production Dynamics with Recurrent Neural Networks

... We presented an analysis of the internal mecha- nism of a model of language production that uses a recurrent neural network at its core. The re- sults show clear patterns of computation that per- mit ... See full document

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Improving Language Modeling using Densely Connected Recurrent Neural Networks

... connected LSTM model with an equal number of parameters outperforms a combination of RNN, LDA and Kneser Ney (Mikolov and Zweig, 2012). Applying Variational Dropout (VD) (Inan et al., 2017) instead of regular dropout ... See full document

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Global solar radiation prediction using recurrent neural networks

... prediction using Fully Recurrent Neural Network (FRNN) and RBF 2014) proved that a better accuracy Several algorithms (Abdelaziz cham El Badoui, 2013) like Gradient Descent back propagation, Gradient ... See full document

5

Translating Videos to Natural Language Using Deep Recurrent Neural Networks

... Our approach to video to text generation is in- spired by the work of Donahue et al. (2014), who also applied a variant of their model to video-to-text generation, but stopped short of training an end-to- end model. ... See full document

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