Vision-based approach for American Sign Language recognition using Edge Orientation Histogram

Hand Gesture Recognition System (HGRS) for detection of American Sign Language (ASL) alphabets has become essential tool for specific end users (i.e. hearing and speech impaired) to interact with general users via computer system. ASL has been proved to be a powerful and conventional augmentative communication tool especially for specific users. ASL consists of 26 primary letters, of which 5 are vowels and 21 are consonants. Proposed Real-time static Alphabet American Sign Language Recognizer- (A-ASLR) is designed for the recognition of ASL alphabets into their translated version in text (i.e. A to Z). The architecture of A-ASLR system is fragmented into six consequent phases namely; image capturing, image pre-processing, region extraction, feature extraction, feature matching and pattern recognition. We have used Edge Orientation Histogram (EOH) in A-ASLR system. The system is developed for detection of ASL alphabets based on Vision-based approach. It works without using colored gloves or expensive sensory gloves on hand. Our A-ASLR system achieves the recognition rate of 88.26% within recognition time of 0.5 second in complex background with mixed lightning condition.