A gesture recognition system for the Colombian sign language based on convolutional neural networks

Sign languages (or signed languages) are languages that use visual techniques, primarily with the hands, to transmit information and enable communication with deaf-mutes people. This language is traditionally only learned by people with this limitation, which is why communication between deaf and non-deaf people is difficult. To solve this problem we propose an autonomous model based on convolutional networks to translate the Colombian Sign Language (CSL) into normal Spanish text. The scheme uses characteristic images of each static sign of the language within a base of 24000 images (1000 images per category, with 24 categories) to train a deep convolutional network of the NASNet type (Neural Architecture Search Network). The images in each category were taken from different people with positional variations to cover any angle of view. The performance evaluation showed that the system is capable of recognizing all 24 signs used with an 88% recognition rate