SIViP (2016) 10:655–662 DOI 10.1007/s11760-015-0790-4 ORIGINAL PAPER On an algorithm for Vision-based hand gesture recognition Dipak Kumar Ghosh 1 · Samit Ari 1 Received: 11 June 2014 / Revised: 5 June 2015 / Accepted: 18 June 2015 / Published online: 30 June 2015 © Springer-Verlag London 2015 Abstract A vision-based static hand gesture recognition method which consists of preprocessing, feature extraction, feature selection and classification stages is presented in this work. The preprocessing stage involves image enhancement, segmentation, rotation and filtering. This work proposes an image rotation technique that makes segmented image rota- tion invariant and explores a combined feature set, using localized contour sequences and block-based features for better representation of static hand gesture. Genetic algo- rithm is used here to select optimized feature subset from the combined feature set. This work also proposes an improved version of radial basis function (RBF) neural network to clas- sify hand gesture images using selected combined features. In the proposed RBF neural network, the centers are automati- cally selected using k-means algorithm and estimated weight matrix is recursively updated, utilizing least-mean-square algorithm for better recognition of hand gesture images. The comparative performances are tested on two indigenously developed databases of 24 American sign language hand alphabet. Keywords American sign language (ASL) hand alphabet · Combined feature · Genetic algorithm (GA) · Hand gesture recognition · Least-mean-square (LMS) algorithm · Radial basis function (RBF) neural network B Dipak Kumar Ghosh dipakkumar05.ghosh@gmail.com Samit Ari samit.ari@gmail.com 1 Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela 769008, India 1 Introduction Development of efficient hand gesture recognition sys- tem is crucial for successful human–computer interaction (HCI) or human alternative and augmentative communica- tion (HAAC) applications like robotics, assistive systems, sign language communication and virtual reality [1–5]. Gestures are communicative, meaningful body motions con- necting physical actions of the fingers, hands, arms, face, head or body with the intent of conveying meaningful infor- mation or interacting with the environment [6]. In general, gestures can be classified into static gestures [1–4] and dynamic gestures [7–9]. Static gesture is described in the form of definite hand configuration or poses, while dynamic gesture is a moving gesture, articulated as a sequence of hand movements and arrangements. However, static ges- tures communicate certain meanings or sometimes act as explicit transition state in dynamic gestures. The sensing techniques which are used in static hand gesture recognition systems include glove-based techniques [10, 11] and vision- based techniques [1, 3, 7]. In glove-based techniques, sensors are utilized to measure the joint angles, positions of the fin- gers and position of the hand in real time [11]. However, gloves are quite expensive, and the weight of the gloves and associated measuring equipment hamper free movement of the hand. Therefore, user interface is complicated and less natural for glove-based techniques [12]. On the other hand, vision-based techniques use one or more cameras to cap- ture the gesture images. Vision-based system may not be robust enough for few applications like gesture-based remote control [13]. However, vision-based techniques provide a natural interaction between humans and computers without using any extra devices [12]. Various features have been reported in the literature [1–4, 14] to represent static hand gesture including features like statistical moments [1], local- 123