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Ismail et al., 2021 - Google Patents

Static hand gesture recognition of Arabic sign language by using deep CNNs

Ismail et al., 2021

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Document ID
13102399304598952992
Author
Ismail M
Dawwd S
Ali F
Publication year
Publication venue
Indonesian Journal of Electrical Engineering and Computer Science

External Links

Snippet

An Arabic sign language recognition using two concatenated deep convolution neural network models DenseNet121 & VGG16 is presented. The pre-trained models are fed with images, and then the system can automatically recognize the Arabic sign language. To …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
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    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
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    • G06K9/6807—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries
    • G06K9/6842—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries according to the linguistic properties, e.g. English, German
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    • G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre

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