Chowdhury, Anika RaisaBiswas, AkashHasan, S.M. FarzanaRahman, Tanjina MehnazUddin, Jia2026-08-202026-08-202017-07-02A. R. Chowdhury, A. Biswas, S. M. F. Hasan, T. M. Rahman and J. Uddin, "Bengali Sign language to text conversion using artificial neural network and support vector machine," 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 2017, pp. 1-4, doi: 10.1109/EICT.2017.8275248.97815386230532-s2.0-85050627207https://hdl.handle.net/10361/29406This paper presents a novel system that converts Bengali Sign language to text using an optimum system comprising of artificial neural networks and support vector machine (SVM). Microsoft Kinect is used to take the input, which is the hand sign performed in front of the camera. The captured hand sign is eventually recognized, after joint and wrist detection and by assessing the contours. Contour feature is extracted and is run through a SVM for classification of the sign. The contour finding algorithm utilizes the convex hull method, and the features extracted after detection is passed through the support vector model for recognition. To validate the performance of the proposed model, a dataset that consists of both male and female hand gesture images is utilized. Experimental results demonstrate 84.11% classification accuracy for our tested dataset.1-4en-USGesture recognitionAssistive technologySupport vector machinesNeural networksFeature extractionHand gestureSign language.Support vector machines.Conference Proceeding10.1109/EICT.2017.8275248