Efficient Noise Reduction and HOG Feature Extraction for Sign Language Recognition

Sign Language is the communication standard for people who have hearing and speaking deficiency, usually called deaf and dumb. It is the only way for such people to communicate. This paper proposes a model which would help in recognizing the different signs in American Sign Language. As this is still an emerging field of research, the dataset available in this topic is very noisy. In this paper, we proposed some image processing based operations such as Logarithmic Transformation, Histogram Equalization etc. to reduce the noise from the images of the dataset. Then, canny edges are detected from the segmented image of signs. After that, the proposed method identifies the signs based on the features extracted using Histogram of Oriented Gradients (HOG) Feature Extraction strategy. The extracted features of the signs are classified using KNN classifier. The experimental result shows that the proposed method offers better classification accuracy (94.23%) in comparison to the method based on BAG of features and SVM (86%).