Mexican Sign Language Recognition: Dataset Creation and Performance Evaluation Using MediaPipe and Machine Learning Techniques

In Mexico, around 2.4 million people (1.9% of the national population) are deaf, and Mexican Sign Language (MSL) support is essential for people with communication disabilities. Research and technological prototypes of sign language recognition have been developed to support public communication systems without human interpreters. However, most of these systems and research are closely related to American Sign Language (ASL) or other sign languages of other languages whose scope has had the highest level of accuracy and recognition of letters and words. The objective of the current study is to develop and evaluate a sign language recognition system tailored to MSL. The research aims to achieve accurate recognition of dactylology and the first ten numerical digits (1–10) in MSL. A database of sign language and numeration of MSL was created with the 29 different characters of MSL’s dactylology and the first ten digits with a camera. Then, MediaPipe was first applied for feature extraction for both hands (21 points per hand). Once the features were extracted, Machine Learning and Deep Learning Techniques were applied to recognize MSL signs. The recognition of MSL patterns in the context of static (29 classes) and continuous signs (10 classes) yielded an accuracy of 92% with Support Vector Machine (SVM) and 86% with Gated Recurrent Unit (GRU) accordingly. The trained algorithms are based on full scenarios with both hands; therefore, it will sign under these conditions. To improve the accuracy, it is suggested to amplify the number of samples.