A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1)

Hand pose tracking is essential in sign languages. An automatic recognition of performed hand signs facilitates a number of applications, especially for people with speech impairment to communication with normal people. This framework which is called ASLNN proposes a new hand posture recognition technique for the American sign language alphabet based on the neural network which works on the geometrical feature extraction of hands. A user’s hand is captured by a three-dimensional depth-based sensor camera; consequently, the hand is segmented according to the depth analysis features. The proposed system is called depth-based geometrical sign language recognition as named DGSLR. The DGSLR adopted in easier hand segmentation approach, which is further used in segmentation applications. The proposed geometrical feature extraction framework improves the accuracy of recognition due to unchangeable features against hand orientation compared to discrete cosine transform and moment invariant. The findings of the iterations demonstrate the combination of the extracted features resulted to improved accuracy rates. Then, an artificial neural network is used to drive desired outcomes. ASLNN is proficient to hand posture recognition and provides accuracy up to 96.78% which will be discussed on the additional paper of this authors in this journal

Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition

This work introduces two novel approaches for feature extraction applied to video-based Arabic sign language recognition, namely, motion representation through motion estimation and motion representation through motion residuals. In the former, motion estimation is used to compute the motion vectors of a video-based deaf sign or gesture. In the preprocessing stage for feature extraction, the horizontal and vertical components of such vectors are rearranged into intensity images and transformed into the frequency domain. In the second approach, motion is represented through motion residuals. The residuals are then thresholded and transformed into the frequency domain. Since in both approaches the temporal dimension of the video-based gesture needs to be preserved, hidden Markov models are used for classification tasks. Additionally, this paper proposes to project the motion information in the time domain through either telescopic motion vector composition or polar accumulated differences of motion residuals. The feature vectors are then extracted from the projected motion information. After that, model parameters can be evaluated by using simple classifiers such as Fisher's linear discriminant. The paper reports on the classification accuracy of the proposed solutions. Comparisons with existing work reveal that up to 39% of the misclassifications have been corrected