Recognition of Persian Sign Language Alphabet Using Gaussian Distribution, Radial Distance and Centroid-Radii
Authors: Abdolmalaki Asra , Ghaderzadeh Abdulbaghi , Maihami Vafa 
Published: January 2020

Aims:
Presenting a novel approach to identify Persian static symptoms in that the proposed sign
language recognition system consists of two segmentation and feature extraction phases. In the segmentation
phase, the hand region is separated by an effective segmentation method from the original image.

Objective:
(1) Introducing an effective framework to solve sensitivity to light conditions in identifying and
recognizing letters of the Persian sign language alphabet. (2) Proposing a new feature extraction method to
eliminate sensitivity to rotation and scale changes. (3) Creating a new dataset includes 480 images of the sign
language alphabet symptoms in order to show the performance of our method.

Methods:
The proposed sign language recognition system uses two segmentation and feature extraction phases.
In the segmentation phase, the hand region is separated by an effective segmentation method from the
original image. This method is based on the unique Gaussian model in the YCbCr color space. The Bayes rule
is used to precisely identify the hand region too. In the feature extraction phase, the radial model is used to
obtain a one-dimensional function to display the hand region boundary and to compute the combined feature
vector. In order to normalize this method, the Fourier Transformation method is applied.

Results:
The system was trained and tested using 480 image samples of Persian sign language characters, 15
images per sign, with the .jpg extension. Extensive experimental evaluations indicate that the proposed recognition
system is less susceptible to displacement, scale, and rotation, and can detect symptoms at an accuracy
of 95.62%.

Conclusion:
In this article, a new system was proposed for recognizing the alphabet letters in Persian sign
language. This system consists of two main phases: the segmentation phase or the hand region identification
and feature extraction phase. In the segmentation phase of the detection system, at first, the hand region is
separated by an effective segmentation method from the original image. This method is based on the single
Gaussian model in the color space of the YCbCr and the Bayes rule. Applying this method in hand segmentation,
enables the system, to recognize the hand region well. In this method, after hand region detection, using
the Sobel edge detector, the image edge is extracted. In the feature extraction phase, the radial distance model
was used to obtain a one-dimensional function display of the hand region boundary and compute its feature
vector. This model is based on the edge and image centroid and computes the centroid distance to the shape
boundary as a function of the angle. Since this model by itself is sensitive to the scale variations and tilt, in
order to normalize this method to tilt variations, the Fourier transformation was applied to feature vector
computed by the radial model. Then the vector elements are divided by the largest vector element, thus, using
this method the computed feature vector will be distinct for different images and will show less sensitivity to
displacement, scale, and gradient. A database consist of 480 images of Persian sign symptoms was created to
the overall accuracy evaluation of the system, the results show that the proposed recognition system can identify
32 letters of the Persian sign language alphabet with a detection rate 95.62%.