Development of Thai Sign Language Detection and Conversion System into Thai with Deep Learning

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Chatchon damrongekarun
Lukket Pisitpipattana
Nuttachot Promrit
Sajjaporn Waijanya


The hearing and speech impaired can communicate using sign language instead of word, which causes obstacles in communicating with unimpaired person. To help the hearing and speech impaired to communicate with the general public, this paper presents. It takes in key points of hands, faces and gestures, using the MediaPipe Framework to detect key points. The information was gathered from video gestures in Thai Sign Language to extract the coordinates of important points. The data were then divided into 80% of the training dataset and 20% of the test dataset. The data were used to train the model using the Long Short-Term Memory (LSTM) neural network algorithm for posture analysis. The model layers and experiment were modified by modifying the parameters. It was found that the model required node addition and class modification. This allowed the model to prevent underfitting problems and increased learning parameters. The accuracy was 0.83 after the results were in Thai words. It will be rendered in a mobile application developed with the Flutter Framework, which connects the model and the application with an API developed with the Flask API.


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damrongekarun, C. ., Pisitpipattana, L. ., Promrit, N. ., & Waijanya, S. . (2023). Development of Thai Sign Language Detection and Conversion System into Thai with Deep Learning. KKU Science Journal, 51(3), 216–225. Retrieved from
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