Polish Sign Language Gestures to Text Conversion Using Machine Learning

There are around 50-100 thousand deaf people in Poland, their main language is Polish sign language. It can be challenging for them to communicate with the rest of society and there is a gap in Polish sign language gestures to text conversion. Although some research has been done before, no research paper or product solves this problem. The primary objective is to develop a concept of an intelligent application that can convert Polish sign language from either a video or a live feed. To achieve this, research was conducted on other sign languages, which helped in selecting the most promising hybrid models of deep neural networks. Subsequently, tests were conducted and the best model was chosen. Finally, the best model was trained on the dataset of Polish sign language, using weights (transfer learning) trained on the MS American Sign Language dataset.

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Publication Details

Journal
Proceedings of the International Conference on Information Systems Development
Published
2024-09-09
DOI
https://doi.org/10.62036/isd.2024.88
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Polish Sign Language Gestures to Text Conversion Using Machine Learning

Łukasz Lemieszewski, Marek Nowak, Jarosław Becker
Proceedings of the International Conference on Information Systems Development
Hand Gesture Recognition Systems
article

Polish Sign Language Gestures to Text Conversion Using Machine Learning

Łukasz Lemieszewski, Marek Nowak, Jarosław Becker
article en

Abstract

There are around 50-100 thousand deaf people in Poland, their main language is Polish sign language. It can be challenging for them to communicate with the rest of society and there is a gap in Polish sign language gestures to text conversion. Although some research has been done before, no research paper or product solves this problem. The primary objective is to develop a concept of an intelligent application that can convert Polish sign language from either a video or a live feed. To achieve this, research was conducted on other sign languages, which helped in selecting the most promising hybrid models of deep neural networks. Subsequently, tests were conducted and the best model was chosen. Finally, the best model was trained on the dataset of Polish sign language, using weights (transfer learning) trained on the MS American Sign Language dataset.

Proceedings of the International Conference on Information Systems Development
The Jacob of Paradies University (PL), VoicePIN.com (Poland) (PL)
Quality Education
Openalex Percentile: Top 85%
Hand Gesture Recognition Systems
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Polish Sign Language Gestures to Text Conversion Using Machine Learning — Łukasz Lemieszewski, Marek Nowak, et al. · Proceedings of the International Conference on Information Systems Development (2024) | TGRS Research Map | TGRS