Bangladeshi Sign Language Recognition Employing Neural Network Ensemble

Recognition of Sign Language Using Neural Networks

Sign Language Recognition

This chapter covers the key aspects of Sign Language Recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gest...

Visual Sign Language Recognition

We have developed the Hand Motion Understanding (HMU) system that understands static and dynamic signs of the Australian Sign Language (Auslan). The HMU system uses a visual 3D hand tracker for motion sensing, and an adaptive fuzzy expert system for classification of the signs. This paper presents the hand tracker that extracts 3D hand configuration data with 21 degrees-of-freedom (DOFs) from a...

Sign Language Recognition

Our goal here is to recognize a sign language measured from wearable sensor gloves. A sign language is expressed as a sequence of gestural patterns to convey a meaning. Hidden Markov models (HMMs) have been shown to be successful in temporal pattern recognition, such as speech, handwriting, and gesture recognition [4]. In this project, we investigate how well HMMs can perform when applied to si...

On Arabic Character Recognition Employing Hybrid Neural Network

Arabic characters illustrate intricate, multidimensional and cursive visual information. Developing a machine learning system for Arabic character recognition is an exciting research. This paper addresses a neural computing concept for Arabic Optical Character Recognition (OCR). The method is based on local image sampling of each character to a selected feature matrix and feeding these matrices...