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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | SIGN LANGUAGE RECOGNITION USING BIDIRECTIONAL LSTM |
| Authors Name: | T. Neetha , Karnam Satwik , Sandi Tejaswi , Lolewar Maitreya |
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IJRTI_186139
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| Published Paper Id: | IJRTI2304155 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Sign language recognition aims to recognize meaningful movements of hand gestures and is a significant solution in intelligent communication between the deaf community and hearing societies. However, until now, the current dynamic sign language recognition methods still have some drawbacks with difficulties of recognizing complex hand gestures, low recognition accuracy for most dynamic sign language recognition, and potential problems in larger video sequence data training. In order to solve these issues, this project presents a multimodal dynamic sign language recognition method based on a deep 3-dimensional residual ConvNet and bi-directional LSTM networks, which is named as BLSTM-3D residual network (B3D ResNet). This method consists of three main parts. First, the hand object is localized in the video frames in order to reduce the time and space complexity of network calculation. Then, the B3D ResNet automatically extracts the spatiotemporal features from the video sequences and establishes an intermediate score corresponding to each action in the video sequence after feature analysis. Finally, by classifying the video sequences, the dynamic sign language is accurately identified. In addition, the B3D ResNet can effectively recognize complex hand gestures through larger video sequence data, and obtain high recognition accuracy for 500 vocabularies from Chinese hand sign language. |
| Keywords: | : Residual Networks, LSTM, Bi-directional LSTM, Convolutional Network, Motion-trajection, Video-sequence, Faster R-CNN, Region Of Interest. |
| Cite Article: | "SIGN LANGUAGE RECOGNITION USING BIDIRECTIONAL LSTM", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.959 - 965, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304155.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2304155
Registration ID:186139
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 959 - 965 Country: Hyderabad, Telangana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2304155 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2304155 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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