Detecting Sign Language from Hand Gestures and Translating it into Text

Objective & Hypothesis:The sign language recognition project to be designed is aimed to be realized in an optimized way using up-to-date technologies.

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Key Findings & Data:The machine learning part of the project will be done over TensorFlow using Keras and Sklearn.

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Conclusion & Impact:The object recognition method to be used was chosen as MediaPipe Holistic.

🔬Design:Modeling & Simulation

📍Scope:Comprehensive Sample

⚙️Variables:Gerçek Zamanlı İşaret Dili Tanıma · Makine Öğrenmesi · Derin Öğrenme

Abstract

The sign language recognition project to be designed is aimed to be realized in an optimized way using up-to-date technologies. The machine learning part of the project will be done over TensorFlow using Keras and Sklearn. TensorFlow was chosen considering the possibility of moving the project to a mobile environment in the future. The object recognition method to be used was chosen as MediaPipe Holistic.

Özet

Tasarlanacak işaret dili tanıma projesinin güncel teknolojiler kullanılarak optimize biçimde gerçeklenmesi amaçlanmıştır. Projenin makine öğrenmesi bölümü Keras ve Sklearn kullanılarak TensorFlow üzerinden yapılacaktır. TensorFlow, ilerleyen aşamalarda projeyi mobil bir ortama taşıma ihtimali göz önünde bulundurularak seçilmiştir. Kullanılacak nesne tanıma yöntemi MediaPipe Holistic olarak seçilmiştir.

Keywords

Gerçek Zamanlı İşaret Dili Tanıma Makine Öğrenmesi Derin Öğrenme Nesne Tanıma Bilgisayarla Görü Real Time Sign Language Recognition Machine Learning Deep Learning Object Recognition Computer Vision

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Cite This Article

KIRCI, P., DURUSAN, B. B., ÖZŞAHİN, B. (2022). Detecting Sign Language from Hand Gestures and Translating it into Text. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 36, 32–35. https://ajindex.com/articles/469721

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References (3)

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