An Automated Bengali Sign Language Recognition System Based on Fingertip Finder Algorithm
Angur M. Jarman, Samiul Arshad, Nashid Alam, and Mohammed J. Islam
This paper presents a new algorithm to identify Bengali Sign Language (BdSL) for recognizing 46 hand gestures, including 9 gestures for 11 vowels,
28 gestures for 39 consonants and 9 gestures for 9 numerals according to the similarity of pronunciation. The image was first re-sized and
then converted to binary format to crop the region of interest by using only top-most, left-most and right-most white pixels.
The positions of the finger-tips were found by applying a fingertip finder algorithm. Eleven features were extracted from each image
to train a multilayered feed-forward neural network with a back-propagation training algorithm. Distance between the centroid of the hand region
and each finger tip was calculated along with the angles between each fingertip and horizontal x axis that crossed the centroid.
A database of 2300 images of Bengali signs was constructed to evaluate the effectiveness of the proposed system, where 70%, 15% and 15% images
were used for training, testing, and validating, respectively. Experimental result showed an average of 88.69% accuracy in recognizing BdSL which is very
much promising compare to other existing methods.
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