## 1 Introduction  
Mexican Sign Language (MSL) serves as the primary mode of communication for over five million deaf individuals in Mexico [1]. Despite its importance, MSL's cultural adoption remains limited, and there is a significant shortage of interpreters, with only 40 certified interpreters available as of December 2021 [1]. These factors pose substantial challenges to the inclusion of the deaf community, creating communication barriers that hinder their full participation in society. Sign languages, including MSL, utilize various signs to convey meaning. According to [PERSON] et al. [2], signs can be classified into three categories: static, dynamic, and continuous dynamic. Static signs involve a single hand configuration without movement, commonly used for representing individual letters. Dynamic signs include specific hand movements to express words, while continuous dynamic signs consist of sequences of dynamic movements that form sentences, lacking clear boundaries between signs. These classifications highlight the complexity and richness of MSL, necessitating different computational approaches for effective recognition. Sign recognition systems can be broadly categorized into three main types: those employing specialized hardware, such as sensor gloves, Kinect [3], or Leap Motion [4]; those based on computer vision techniques; and hybrid systems that combine both approaches. While specialized hardware can capture fine-grained motion data, it is often costly and impractical for everyday use. On the other hand, computer vision-based methods rely on image processing and machine learning techniques to detect and classify hand gestures without needing additional devices, making them more accessible. Several sign recognition systems have been developed for MSL, each with different approaches and varying degrees of success. For instance, [PERSON] et al. [4] proposed a system for recognizing vowels in MSL using Leap Motion to capture hand key points, achieving 100% accuracy with a Multilayer Perceptron (MLP) classifier. In a similar vein, [PERSON] [5] utilized Kinect hardware to classify alphabet signs with an evolutionary matrix-based classifier, attaining 90% accuracy while accounting for lighting variations, though the system did not support the letters J and N.  
Other methods have explored the use of machine learning techniques for sign classification. For example, [PERSON] et al. [6] achieved 86% accuracy for 24 alphabet signs using a support vector machine (SVM) trained with artificially generated data. [PERSON] et al. [7] demonstrated 100% accuracy for 21 alphabet signs using Kinect, though this system was limited to controlled laboratory conditions. Additionally, [PERSON] and [PERSON] [8] implemented convolutional neural networks (CNNs) with transfer learning to recognize static signs, achieving an accuracy of 91.56%, but required users to take a picture for each letter. In another approach, [PERSON] and [PERSON] [9] combined Kinect hardware with artificial neural networks, reaching 90.27% accuracy for alphabet classification. These studies highlight the progress in MSL recognition but also reveal limitations, particularly in the reliance on specialized hardware and the challenges associated with recognizing dynamic signs or operating in uncontrolled environments. The present work aims to address these gaps by developing a computer vision-based system capable of recognizing both static and dynamic signs in MSL, including the dynamic letters \"k\", \"rr\", and \"ll\". This approach seeks to improve the accessibility and usability of sign language recognition technology, making it more practical for real-world applications and supporting the inclusion of the deaf community in everyday communication. Tables 1 and 2 provide a comprehensive review of the published studies from 2011 to the present, focusing exclusively on works that report results for the MSL alphabet.