Real-time computer vision-based Bengali Sign Language recognition

This paper presents a real-time computer vision-based Bengali Sign Language (BdSL) recognition system. The system detects the probable hand from the captured image. The system uses Haar-like feature-based cascaded classifiers to detect the hand in each frame. From the detected hand area, the system extracts the hand sign based on Hue and Saturation value corresponding to human skin color. After normalization the system converts the hand sign to binary image. Then the binary images are classified by comparing with pre-trained binary images of hand sign using K-Nearest Neighbors (KNN) Classifier. The system is able to recognize 6 Bengali Vowels and 30 Bengali Consonants. The system is trained using 3600 (36×10×10) training images where each of 10 signers performed 10 signs for each corresponding Bengali alphabet and the system is tested using another 3600 (36×10×10) images of 10 signers. The system is achieved recognition accuracy of 98.17% for Vowels and 94.75% for Consonants.