Computer vision based Bengali sign words recognition using contour analysis

This paper presents a computer vision based Bengali sign words recognition system using contour analysis. Haar-like feature based cascaded classifier is used to locate the predefined hand posture (Opened Hand and followed by Closed Hand postures) from the captured image, and bounded by a rectangular box that is initialized as region of interest (ROI). The system follows this ROI, crops it and normalizes into predefined size. The system segments skin-like area based on Hue and Saturation value from the normalized image. Then the system employs morphological operations and Gaussian smoothing to remove noises, and then converts it into gray image. The system extracts contours using Canny edge detector and encodes extracted contours into Vector Contours (VC). After scaling VC into predefined size, the system generates feature space based on equalized VC, value of normalized Auto-Correlation Function (ACF) and ACF descriptors for each sign word that will be used for training and/or testing process. The system recognizes sign words based on maximum similarity between tests and predefined training contour templates using Inter-Correlation Function (ICF). The system is trained and tested using 1800 (18×10×10) contour templates separately for 18 Bengali sign words from 10 signers achieving recognition accuracy of 90.11% with computational cost of 26.063 milliseconds per frame.