Abstract
This paper discusses a Zernike Moment (ZM) based feature vector that can characterize the alphabets of Indian Sign Language (ISL). Sign Language Recognition (SLR) is a multiclass shape classification problem. Studies of human visual system reveal that while observing any scene, the focus is more on the center part and it decreases toward the edges. This became the basis of calculating the ZMs on a unit circular disk. Continuous orthogonal moments such as magnitude of ZM are well known for their shape representing capabilities. However, for a SLR system the highest order of moments is to be estimated, because with increase in order of moments, the feature vector size increases significantly. In order to find the maximum order that is sufficient to classify the shapes of hand silhouettes, performance of various classifiers is analyzed. Results show that increasing the order of ZM, beyond a certain order does not contribute to the improvement in recognition capability. The results improve when the ZM is combined with some basic geometric features and commonly used shape descriptors such as Hu moments (HMs).
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