# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 5 Conclusions  
A significant portion of the global population struggles with hearing impairments, and image classification models can potentially assist the deaf and hard of hearing by translating sign language. During our research, we explored the state-of-the-art for sign language recognition. While our study found that sign language recognition is well-researched, we identified a significant gap in the literature concerning NSL recognition. To address this gap, we conducted a comparative analysis of various machine learning models to provide further research to determine the most effective classification model for NSL recognition. This research aims to assist in communication capabilities for the deaf and hard of hearing in Norway and hopefully take a step closer to implementing machine learning models to help the deaf and hard of hearing, reducing the communication gap between those who are deaf and hard of hearing and those who are not.  
Our literature review revealed several popular machine learning models for sign language recognition. Based on the literature, we chose KNN, CNN, and SVM for our comparative analysis as they represent different paradigms in machine learning and have demonstrated promising results in various applications. Our research aimed to effectively compare these models' data preparation techniques, accuracy, and computational efficiency when applied to NSL recognition.  
We created a dataset specifically for NSL recognition. The dataset consists of 24,300 images covering 27 of the 29 NSL alphabet signs, with varying lighting conditions, backgrounds, and hand orientations performed by both male and female signers. The dataset's comprehensiveness and diversity make it a valuable resource for future research in NSL recognition. It also provides a strong foundation for developing and testing machine learning models in this field.  
The study conducted a detailed comparative analysis of three popular machine learning models: KNN, CNN, and SVM. This analysis provided insights into the strengths and weaknesses of each model for NSL recognition. It also provided valuable insight into the effectiveness of different data preparation techniques, such as pre-processing, segmentation, and feature extraction. This will assist future researchers of NSL recognition, and we see this contribution as a benchmark for future work.  
Based on the comparative analysis, the research identified CNN and SVM as the best-performing models for NSL recognition regarding accuracy, efficiency, adaptability, and scalability. This finding provides a foundation for future research to implement, refine, improve, and scale these models for practical applications.  
The contribution and findings of this paper serve as an incentive for future research and development of advanced, accessible, and cost-effective solutions for NSL recognition. Our work also emphasizes the need for such recognition systems in Norway to improve communication between the deaf and hard of hearing and the hearing majority, thereby reducing communication barriers and enhancing overall accessibility.  
**Author Contributions:** Conceptualization, B.S. and S.K.; methodology, B.S. and S.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.