Bangladeshi Sign Language Recognition using fingertip position

Sign language is the only means of communication for deaf and dump people which uses manual communication and body language to convey meaning. For any sign language, an interpreter is essential to communicate with deaf and dump people. To enhance interaction with community, Sign Language Recognition (SLR) is a growing field of research now a days. The task of SLR is language specific and a number of prominent works are available for few major languages. On the hand, the works are very few for Bangladeshi Sign Language (BSL) although Bangla is a major language and Bangladesh has a large community of deaf and dump people. In this study a BSL recognition scheme has been investigated based on fingertip position. The method considered relative tip positions of five fingers in two dimension space and position vectors are used to train artificial neural network (ANN) for recognition purpose. The method seems efficient with respect to ANN training with pixel values of image as of previous studies. The proposed method has been tested on a prepared data set of 518 images of 37 signs and achieved 99% recognition rate. The proposed method is found better than exiting BSL recognition methods.

[1]  Alex Pentland,et al.  Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Karen Das,et al.  Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique , 2013, ArXiv.

[3]  Atiqur Rahman.  Recognition of Static Hand Gestures of Alphabet in Bangla Sign Language , 2012 .

[4]  P. Subha Rajam,et al.  Recognition of Tamil Sign Language Alphabet using Image Processing to aid Deaf-Dumb People , 2012 .

[5]  Mahmood Jasim,et al.  Computer vision based Bengali sign words recognition using contour analysis , 2015, 2015 18th International Conference on Computer and Information Technology (ICCIT).

[6]  Nitin V. Pujari,et al.  Finger Detection for Sign Language Recognition , 2009 .

[7]  Ali Karami,et al.  Persian sign language (PSL) recognition using wavelet transform and neural networks , 2011, Expert Syst. Appl..

[8]  Kazi Md. Rokibul Alam,et al.  Bangladeshi Sign Language Recognition employing Neural Network Ensemble , 2012 .

[9]  Oliver Sacks,et al.  The Language of the Deaf , 1991 .

[10]  Mohamed F. Tolba,et al.  A proposed PCNN features quality optimization technique for pose-invariant 3D Arabic sign language recognition , 2013, Appl. Soft Comput..

[11]  Chen Wei,et al.  New three-dimensional space vector pulse width modulation of PV-AF system based on the gh$\gamma $ coordinate system , 2015 .

[12]  Ming C. Leu,et al.  American Sign Language word recognition with a sensory glove using artificial neural networks , 2011, Eng. Appl. Artif. Intell..

[13]  Neelesh SARAWATE,et al.  A real-time American Sign Language word recognition system based on neural networks and a probabilistic model , 2015 .

[14]  Shaikh Anowarul Fattah,et al.  Real-Time American Sign Language Recognition Using Skin Segmentation and Image Category Classification with Convolutional Neural Network and Deep Learning , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[15]  J. Canny.  Finding Edges and Lines in Images , 1983 .

[16]  Sharifa Rania Mahmud.  Survey on Convex Drawing of Planar Graph , 2013 .

[17]  Mohamed F. Tolba,et al.  Arabic sign language continuous sentences recognition using PCNN and graph matching , 2012, Neural Computing and Applications.