A New System for Automatic Recognition of Italian Sign Language

This work proposes a preliminary study of an automatic recognition system for the Italian Sign Language (Lingua Italiana dei Segni - LIS). Several other attempts have been made in the literature, but they are typically oriented to international languages. The system is composed of a feature extraction stage, and a sign recognition stage. Each sign is represeted by a single Hidden Markov Model, with parameters estimated through the resubstitution method. Then, starting from a set of features related to the position and the shape of head and hands, the Sequential Forward Selection technique has been applied to obtain feature vectors with the minimum dimension and the best recognition performance. Experiments have been performed using the cross-validation method on the Italian Sign Language Database A3LIS-147, maintaining the orthogonality between training and test sets. The obtained recognition accuracy averaged across all signers is 47.24%, which represents an encouraging result and demonstrates the effectiveness of the idea.