This paper presents the implementation of a Region-based Convolutional Neural
Network focused on the recognition and localization of hand gestures, in this case
2 types of gestures: open and closed hand, in order to achieve the recognition of
such gestures in dynamic backgrounds. The neural network is trained and validated,
achieving a 99.4% validation accuracy in gesture recognition and a 25% average
accuracy in RoI localization, which is then tested in real time, where its operation
is verified through times taken for recognition, execution behavior through trained
and untrained gestures, and complex backgrounds.