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Sign language is a very important communication tool for hearing-impaired people and also for the communication between hearing-impaired and non-handicapped people. There are many methods for sign language recognition, some of which are based on Hidden Markov Model (HMM) and others are based on Support Vector Machine (SVM) and so forth. In fact, the most of previous methods recognize fingerspelling...
Identifying hand configuration is a critical feature of sign language translation. In this paper, we describe our approach to recognize hand configurations in real time with the purpose of providing accurate predictions to be used in automatic sign language translation. To capture the hand configuration we rely on data gloves with 14 sensors that measure finger joints bending. These inputs are sampled...
The sign language considered as the main language for deaf and dumb people. So, a translator is needed when a normal person wants to talk with a deaf or dumb person. In this paper, we present a framework for recognizing Bangla Sign Language (BSL) using Support Vector Machine. The Bangla hand sign alphabets for both vowels and consonants have been used to train and test the recognition system. Bangla...
Deaf people use systems of communication based on sign language and finger spelling. Manual spelling, or finger spelling, is a system where each letter of the alphabet is represented by an unique and discrete movement of the hand. RGB and depth images can be used to characterize hand shapes corresponding to letters of the alphabet. The advantage of depth cameras over color cameras for gesture recognition...
We propose a novel approach for solving the Chinese manual alphabet in vision. Rather than focusing on local features and their consistencies in the images data, our approach aims at extracting both the global and local features of an image. Features calculated from gray-level co-occurrence matrix and other multi-features are introduced for the classifier to characterize the various visual properties...
Expressions carry vital information in sign language. In this study, we have implemented a multi-resolution active shape model (MR-ASM) tracker, which tracks 116 facial landmarks on videos. Since the expressions involve significant amount of head rotation, we employ multiple ASM models to deal with different poses. The tracked landmark points are used to extract motion features which are used by a...
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