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Recognition Static Hand Gestures of Alphabet in ASL
| Content Provider | CiteSeerX |
|---|---|
| Author | Rahman, Atiqur Aktaruzzaman |
| Abstract | Abstract — This paper presents a system for recognizing static gestures of alphabet in American Sign Language (ASL) using artificial neural network (ANN). The required images for the selected alphabet are obtained using a digital camera. The color images are then cropped, resized, and converted to binary images.Then height, area, centroid, and distance of the centroid from the origin (top-left corner) of the image are used as features. Finally, the extracted features are used to train a Backpropagation NN. This recognition system does not use any gloves or visual marking systems. This system only requires the images of the bare hand for the recognition. Experimental results show that this system is able to recognize 26 selected ASL alphabets with an average accuracy of 80.28 %. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Recognition Static Hand Gesture Binary Image Color Image Top-left Corner Static Gesture Digital Camera Average Accuracy Visual Marking System Recognition System American Sign Language Asl Alphabet Bare Hand Extracted Feature Required Image Experimental Result Backpropagation Nn Artificial Neural Network |
| Content Type | Text |
| Resource Type | Article |
