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Developing a Vision-Based Continuous Iranian Sign Language Translation System

Ghadami, Ali | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56238 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Taheri, Alireza; Meghdari, Ali
  7. Abstract:
  8. Sign language is an essential means of communication for millions of people around the world and serves as their primary language. However, most communication tools and technologies are designed for spoken and written languages, which can create barriers and limitations for the deaf community. By creating a sign language recognition system, we can bridge this communication gap and enable people who use sign language as their primary mode of expression to communicate better with people and their surroundings. This sign language recognition system increases the quality of education, the quality of health services, improves public interactions and creates equal opportunities for the deaf community. In this research, an attempt will be made to continuously recognize Iranian sign language with the help of the latest machine learning tools such as transformer networks. The first step in this research is to collect Iranian sign language data at the word and sentence level, which is very valuable for Iranian sign language due to the lack of these data. The translation and recognition of sign language sentences has been investigated through two ways. The first path is sentence recognition through single word recognition model and adaptive windowing technique, in which genetic algorithm is used to find the optimal architecture and fuzzy controller is used to change the window length. The second path is the direct recognition of the sentence in one place. The implementing and training the models led to 90.2% accuracy for single word recognition and acceptable performance in the sentence recognition section with windowing and direct methods, so that 17 sentences out of 20 test sentences in the windowing method and 115 sentences out of 150 test sentences in the direct method are detected as completely correct or with only one mistake in the words. Finally, the sign language training software that allows real-time feedback to users with the help of developed models is introduced. This software, and this research in general, is an important step in the practical implementation of sign language recognition models in the real world, which can greatly help the deaf
  9. Keywords:
  10. Deep Learning ; Artificial Intelligence ; Machine Vision ; Genetic Algorithm ; Sequence to Sequence Translation ; Persian Sign Language

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