MediaPipe and Its Suitability for Sign Language Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU148801" target="_blank" >RIV/00216305:26210/23:PU148801 - isvavai.cz</a>
Result on the web
<a href="https://www.engmech.cz/improc/2023/251.pdf" target="_blank" >https://www.engmech.cz/improc/2023/251.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
MediaPipe and Its Suitability for Sign Language Recognition
Original language description
The paper presents the framework MediaPipe as a tool to extract pose features for the task of word-level isolated sign language recognition. It tests the framework’s suitability on the state-of-the-art sign language dataset AUTSL. Extracted sequences of pose features are classified by the Long Short-Term Memory recurrent neural network constructed with the TensorFlow computational library. The paper describes the proposed method flow, preprocessing of the extracted features, and training. Obtained results are then validated on test datasets, and the impact of face landmarks is evaluated. The top-1 accuracy with face landmarks is 49.89 %, while 53.21 % without them.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů