# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 2 Literature Review
### Key Findings  
The finding in our literature search reveals that sign language recognition is a well-researched topic. Primarily for static sign language recognition models, there was a lot of research on KNN, CNN, and SVM models. Based on our summary in Table 1, we can see that the segmentation varied across all three classification models. Still, HOG was very popular for feature extraction across all the models. CNN seems to have a lot of research that does not include segmentation or feature extraction of their datasets. Still, they usually balance that out with quality pre-processing and large datasets, one of the most important aspects of CNN models. The accuracy of all the models is very good, and there is no clear indication of which model best suits sign language recognition.  
During our research, we found some gaps in the literature that we hope we can assist. Upon studying the literature, there is no research provided on Norwegian Sign Language. We aim to close this gap by creating SVM, KNN, and CNN models for Norwegian Sign Lan  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Segmentation** & **Feature Extraction** & **Classification** & **Dataset** & **Accuracy** \\ \hline ([PERSON], [PERSON], and [PERSON] 2016) & \multirow{4}{*}{NaN} & \multirow{4}{*}{SIFT and HOG} & HOG-SVM to categorize images as single or two-handed gestures, then KNN for classification & 780 ISL images of one-handed gestures, then KNN alphabets & 98.33\% for one-handed \\ \cline{3-6} ([PERSON] et al. 2018) & & & HOG & KNN & 26 alphabets & 94.23\% \\ \hline ([PERSON], [PERSON], and [PERSON] 2018) & \multirow{4}{*}{NaN} & \multirow{4}{*}{SIFT and HOG} & \multirow{4}{*}{CNN} & \multirow{4}{*}{25 alphabets} & 100\% on test data 98.05\% on real-time \\ \cline{3-6} ([PERSON] et al. 2019) & & & & & \\ \cline{3-6} ([PERSON] et al. 2019) & & & & & \\ \cline{3-6} ([PERSON] et al. 2019) & & & & & \\ \cline{3-6} ([PERSON] et al. 2019) & & & & & \\ \cline{3-6} ([PERSON] et al. 2019) & & & & & \\ \cline{3-6} ([PERSON] et al. 2019) & & & & & \\ \cline{3-6} ([PERSON] et al. 2019) & & & & & \\ \cline{3-6} ([PERSON] et al. 2022) & & & & & \\ \hline ([PERSON] et al. 2022) & \multirow{4}{*}{NaN} & \multirow{4}{*}{NaN} & \multirow{4}{*}{CNN} & 34,627 ASL images on 26 alphabets & 99.67\% \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \hline ([PERSON], [PERSON], and [PERSON] 2020) & \multirow{4}{*}{NaN} & \multirow{4}{*}{Multilevel-HOG} & \multirow{4}{*}{SVM} & 2600 ISL images on 26 grayscale alphabets & 92\% accuracy on ISL \\ \cline{3-6} ([PERSON] et al. 2020) & & & & with complex background & \\ \cline{3-6} ([PERSON] et al. 2020) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \cline{3-6} ([PERSON] et al. 2021) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of finding different machine learning models.  
guage recognition. Another gap we identified was that many of the datasets the researchers used to train their models were simple images with non-complex backgrounds. While this will achieve great accuracy for the models, an important aspect of image recognition is to build robust models that can generalize. If the models are trained on simple images, it is tough to see how these can be used in real-life applications. We, therefore, aim to create our NSL model based upon a combination of non-complex and complex images with different lighting and backgrounds to develop models that would generalize well. While this might negatively affect our model's accuracy, we hope it is better suited for real-life situations to further assist the deaf and hard of hearing in Norway.