Sign Language Recognition with Ensemble Learning and Bayesian Optimization: A Deep Learning-Based Approach

Authors : Andaç Fındıkçı, Musa Balcı, Hüseyin Aydilek, Mustafa Yasin Erten

Pages : 393-409

Doi:10.29132/ijpas.1637971

View : 93 | Download : 194

Publication Date : 2025-12-29

Article Type : Research Paper

Abstract :Communication has undergone a continuous evolution as one of the most fundamental needs in human history. Initially, communication was established through body language and gestures, but it became more complex over time with the development of spoken language. The invention of writing marked a revolutionary milestone in the history of communication. However, this rapid advancement also brought about communication challenges. In today’s world, numerous studies focus on addressing these issues and finding effective solutions. Technological advancements and artificial intelligence hold significant potential for solving communication problems. Notably, the difficulties in communicating with individuals who are hearing impaired have become a prominent area of focus. In this study, a model was developed using artificial intelligence algorithms to facilitate communication through sign language, specifically detecting American Sign Language. The model was created using deep learning architectures such as InceptionV3, DenseNet169, and VGG16, and trained on a dataset sourced from Kaggle. The results were combined using the ensemble learning method. The performance of the models was optimized through Bayesian search optimization algorithm and evaluated using metrics derived from confusion matrices. The findings revealed that ensemble learning models demonstrated superior performance, indicating that this model could serve as an effective tool in improving communication with hearing-impaired individuals.
Keywords : Topluluk öğrenmesi, bayes hiperparametre optimizasyonu, InceptionV3, DenseNet169, VGG16

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