## 4 Conclusions  
This study successfully classified the letters of the MSL alphabet, including the dynamic signs for \"k\", \"ll\", and \"rr\". Unlike many existing approaches in the literature, our solution does not require specialized hardware, which enhances its practicality for everyday use. The use of the MediaPipe API was crucial in detecting hand key points from the images, despite a loss of approximately 50% of the raw images during preprocessing. Nevertheless, the final classifier, as depicted in Figure 8, proved to be computationally efficient and achieved an F1 score of 90.91%, precision of 91.25%, recall of 91.05%, and accuracy of 91.09% across all signs. A notable strength of this work is its ability to perform sign classification in unstructured environments, allowing for variations in height, position, and lighting conditions. This flexibility distinguishes it from other methods that often require controlled settings, demonstrating the robustness of our approach under real-world conditions. The findings also highlight that static hand configurations can adequately represent most MSL signs, with exceptions for certain dynamic letters. This insight simplifies the classification task by allowing static approaches for a broader range of signs. Moreover, our work is one of a few to incorporate double letters such as \"ll\" and \"rr\", addressing a gap in the current literature and providing a more complete representation of the MSL alphabet. The approximately 50% data loss during the MediaPipe preprocessing phase suggests an avenue for future research. Investigating the conditions under which the API's performance degrades, such as specific lighting setups, could lead to improved preprocessing techniques and higher data retention rates. Additionally, integrating more sophisticated imputation methods may help address the issue of missing data and further enhance model accuracy. Signed languages pose a significant computational challenge, particularly when dealing with continuous dynamic signs, which involve sequences of movements. Our future work will focus on obtaining or creating a dataset that includes continuous dynamic signsand developing a hybrid algorithm, inspired by the one used here, to accurately classify these complex sequences. By extending this approach to continuous signs, we aim to further bridge the communication gap between the deaf and hearing communities. In conclusion, this work contributes to the field of sign language recognition by offering a flexible, hardware-independent solution for MSL, with promising results that encourage further exploration of dynamic sign classification in real-world conditions. Conceptualization, A.L.-C., M.A.M.-A. and H.C.; methodology, A.L.-C., M.A.M.-A. and H.C.; software, A.L.-C.; validation, M.A.M.-A. and H.C.; formal analysis, M.A.M.-A., A.L.-C. and H.C.; investigation, A.L.-C., M.A.M.-A. and H.C.; resources, M.A.M.-A. ; data curation, A.L.-C.; writing--original draft preparation, A.L.-C. and M.A.M.-A. ; writing--review and editing, M.A.M.-A. and H.C.; visualization, A.L.-C.; supervision, M.A.M.-A. ; project administration, M.A.M.-A. ; funding acquisition, M.A.M.M.-A. All authors have read and agreed to the published version of the manuscript. This work was supported by Instituto Politecnico Nacional (IPN) through Secretaria de Investigacion y Posgrado (SIP-IPN) research grants SIP-2259, SIP-20240666, SIP-20240610; Comision de Operacion y Fomento de Actividades Academicas del IPN (IPN-COFAA) and Programa de Estimulos al Desempeno de los Investigadores (IPN-EDI) and Consejo Nacional de Humanidades, Ciencias y Tecnologias, Sistema Nacional de Investigadores (CONAHCYT-SNII).