# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 3 Methods
### Image Acquisition  
To build our NSL (Norwegian sign language) recognition model, we had to acquire a dataset containing images or videos of NSL. During our extensive research, we could not find any public NSL dataset and therefore had to start creating our own from scratch. The first step in creating our NSL dataset was determining which sign language category  
Figure 1: Flowchart illustrating the pipeline of our method. we wanted to create. Because little to no research has been conducted on NSL recognition, we decided that the best approach would be to create a static fingerspelling dataset of the NSL alphabet. We chose this sign language category because languages are built from the ground up, starting with the alphabet. Therefore, we wanted to create a strong base for further work in NSL recognition by creating a baseline for NSL alphabet recognition. The signs must be performed and statically read to develop a static fingerspelling sign language model. The Norwegian Sign Language (NSL) includes 29 signs from A to A. However, the letters A and H cannot be statically displayed, so they were excluded from our research. Static images can display the other 27 signs, most of them as one-handed, but J, P, Q, U, X, Z, and O had to be represented as two-handed as there is no static one-handed version. All information about the NSL alphabet and the drawn images in Figure 2 was gathered from the Norwegian Sign Language Dictionary, www.tegnordbok.no accessed on 11 July 2023, which was developed by Statped (State Special Needs Education Service) on behalf of the Norwegian Directorate for Education and Training [27]. For our dataset, we created 900 images for each of the 27 NSL alphabet signs, resulting in 24,300 images. The images of one female and one male were taken to ensure that the model could generalize better. We captured 600 images inside with natural and artificial lighting on a white background. Of those, 300 images were taken by the female and 300 by the male. We also captured 300 more images with a complex background to help the model better handle real-world scenarios. These images were taken by the male participant indoors with various objects in the background. Table 2 shows the distribution of the dataset, and Figure 3 illustrates the different scenarios. We also ensured that the dataset was balanced and that all the classes had the same number of pictures, as this is an essential step in making recognition models that are good at generalizing and aim for a high accuracy [28]. While different backgrounds and people are important steps in creating a dataset for a well-generalized model, we also ensured that the signs we performed during our image acquisition consisted of different angles and rotations of the different signs. In the same way that people pronounce words differently with their voice, signers have different hand orientations when they perform sign language gestures. By incorporating rotated images in the dataset, we can help the model learn to recognize the gestures regardless of the orientation of the signer's hands. Examples of signs with different angles are shown in Figure 4, and different rotations are illustrated in Figure 5.  
Based on the literature we have gathered on sign language recognition, a dataset consisting of 24,300 images represents a substantial baseline for most machine learning models. It should be noted that specific machine learning models may not optimally perform when trained on excessively large datasets. Hence, we decided not to create a larger dataset, recognizing that various machine learning models have different limitations and requirements. In the case of machine learning models such as CNN, which require a considerable amount of data for effective training, data augmentation techniques can be employed to expand the dataset size as needed. Figure 2: Drawn images and captured images of each sign were used in our comparative analysis.