Recognition of Indian Sign Language using feature fusion

Author

Agrawal, S.C. ; Jalal, Anand Singh ; Bhatnagar, Charul

Author_Institution

Dept. of Comput. Eng. & Applic., GLA Univ., Mathura, India

fYear

2012

Firstpage

1

Lastpage

5

Abstract

Sign Language is the most natural and expressive way for the hearing impaired. This paper presents a method for automatic recognition of two handed signs of Indian Sign Language (ISL). The method consists of three phases: Segmentation, Feature Extraction and Recognition. The segmentation is done through Otsu´s algorithm. In the feature extraction phase, shape descriptors, HOG descriptors (Histogram of Oriented Gradient) and SIFT (Scale Invariant Feature Transform) feature have been fused to compute a feature vector. In the recognition phase, a multi-class Support Vector Machine (MSVM) is used for training and classifying signs of ISL. The experimental results provide evidence of the effectiveness of the proposed approach with 93% recognition rate.

Keywords

feature extraction; gradient methods; handicapped aids; image fusion; image segmentation; natural language processing; palmprint recognition; shape recognition; sign language recognition; support vector machines; wavelet transforms; HOG descriptor; ISL; Indian sign language recognition; MSVM; Otsu´s algorithm; SIFT; automatic handed sign recognition; feature extraction; feature fusion; feature vector; hearing impaired; histogram of oriented gradient; image segmentation; multiclass support vector machine; scale invariant feature transform; shape descriptor; Assistive technology; Feature extraction; Gesture recognition; Histograms; Image segmentation; Shape; Vectors; Indian sign language; histogram of oriented gradient; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Human Computer Interaction (IHCI), 2012 4th International Conference on

Conference_Location

Kharagpur

Print_ISBN

978-1-4673-4367-1

Type

conf

DOI

10.1109/IHCI.2012.6481841

Filename

6481841