Prototype of real-time Mexican sign language classifier

Abstract

Mexican sign language (MSL) is the language used by the deaf community in Mexico. Like Spanish, it has its own distinct grammar, syntax, and vocabulary. However, instead of relying on sounds, MSL conveys meaning through gestures, facial expressions, and body movement. This research proposes the creation of an image dataset of the MSL alphabet for real-time sign detection. A neural network model was developed to recognize these signs, achieving an accuracy of approximately 60%. Although this result is modest, the study establishes a foundation for future work that could facilitate communication for MSL users or lead to the development of educational applications for language learning.