## 2 Our Proposal
### Full Alphabet Dataset Creation  
To establish the different ways to represent the SML letters, Table 3 presents this classification [21]; it is essential to note that double letters are considered in a different classification since they have the particularity that they start from the simple letter and then move the hand to indicate that it is a double letter. Figure 2 shows all the steps to obtain our dataset. We accomplish this task in two separate processes: one for static signs and another for dynamic signs. In the end, they are merged. Starting with 1a, we captured 18,267 images of the 26 static LSM letters performed by three people in different environments to vary lighting conditions within the scene and the environment. Figure 1: Main Methodology. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Letters** & **Letters** \\ \hline Static & 21 & A, B, C, D, E, F, G, H, I, L, M, \\ Dynamic & 6 & N, O, P, R, S, T, U, V, W, Y \\ Dynamic (double letters) & 2 & LL, RR \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sign classification of MSL Alphabet. Next, in step 1b, the API MediaPipe [22] has multiple solutions to computer vision activities such as pose estimation, face detection, and hand detection, among others. We discard images in which MediaPipe does not detect the existence of a hand within the image with confidence more significant than 50%. Then, the expert labels each image with its respective letter in Spanish 1c, and in step 1d, we preprocess each image to convert it to a vector called coordNorm; this process is described in detail in Section 2.2. Finally, we stored all these vectors in the \"26 static letters subset\" subset. Figure 3 shows letters e, r, w, i, and f, which are static signs; note that specific configurations of the fingers are similar, for example, r and w, which increases the difficulty of correct detection. On the other hand, Figure 4 shows dynamic signs that are considered static; for example, the letter x has a back-and-forth movement, but the hand shape is unique. Therefore, with the help of an LSM expert, it is determined that it is sufficient to detect the hand shape to differentiate it from other signs. Figure 4: Dynamic letters that are used as static ones. Figure 3: Examples of static signs. Figure 2: Full Alphabet dataset creation. On the other hand, for the creation of the dataset of dynamic signs, only one sign performer was used in a combined illuminated environment, where we captured 50 videos for letters k, rr, and ll in step 1e. Subsequently, in step 1f, those frames of the videos where MediaPipe failed to detect a hand are removed, and in step 1g, each of these videos is labeled by an expert. The same image preprocessing is used in step 1d to obtain the subset \"k & ll & rr\". It is essential to clarify that 100% of these data are part of the final test set. Figure 5 presents the sequence for the letter k. Note that this dynamic sign starts and ends with the static sign of the letter p, so the challenge is to correctly detect the three images marked with [*] that represent the transition that allows the distinction of the letter k from the letter p. The k sign is the only one in the alphabet with the same hand shape as another, the p sign, so it cannot be taken as a static sign like those presented in Figure 4.  
At the end, the images that pass this validation become part of the dataset 1h. Of the total number of images in 1h, we divided them into three subsets: learning for AI algorithms (50%), validation for AI algorithms (20%), and the final test (30%), used by the steps in Figure 6. These percentages refer only to static signs. Some crude image examples are shown in Figure 6 where the considered lighting conditions are listed as follows:  
1. Natural lighting: Light from the sun is available for and against. Figure 6a shows images with positive and negative sunlight conditions without a specific background. 2. Artificial lighting: Lamps provide the primary source of light.