报告详情
Detecting Sign language gesture for Deaf and Mute using InceptionV3 MODEL
编号：118
访问权限：仅限参会人
更新：2026-07-22 16:10:08 浏览：19次
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摘要
Effective communication is essential for social inclusion, yet individuals with hearing or speech impairments often face communication barriers when sign language is not understood by the wider community. In this study, an automated sign language gesture recognition model was developed to translate static hand gesture images into written text. The Sign Language MNIST dataset was used, which contains 27,455 training images and 7,172 testing images across 24 American Sign Language alphabet classes. Each grayscale image was converted into a three channel RGB representation, resized to 299×299 pixels and normalized before classification. Transfer learning was applied using the InceptionV3 architecture with ImageNet pretrained weights and the model was trained using the Adam optimizer with a learning rate of 0.0001. Performance was evaluated using accuracy, precision, recall, F1-score and a confusion matrix. The proposed model achieved 99% training accuracy and 100% accuracy on the official testing split. These results indicate that InceptionV3 can extract discriminative visual features from static hand gesture images. Nevertheless, validation on more diverse real world images and continuous video streams is needed before the system can be considered ready for practical use.
关键词
deaf and mute,neural network,InceptionV3,sign language gestures,Bistable Stochastic Resonance; Wavelet Transform; Image Processing,deep learning
报告人
Baseldbwan Baseldbwan
ASSIT. PROFESSOR ALBAHA PRIVATE COOLEGE OF SCIENCE稿件作者
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重要日期
-
会议日期
07月30日
2026
至08月01日
2026
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07月28日 2026
初稿截稿日期
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08月03日 2026
注册截止日期
主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
IEEE Madras Section
联系方式
- ICNCCom 2026
- ic******@usssociety.org
历届会议
-
2026年07月30日 印度 Trichy
2026 International Conference on Networks Computers and Communications
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