Appearance-Based Features for Automatic Continuous Sign Language Recognition
نویسندگان
چکیده
This diploma thesis investigates appearance-based features for the person-independent vision-based recognition of continuous sign language. A large variety of methods which have been successfully used for automatic speech recognition is applied to this task. Appearance-based approaches do not rely on a segmentation of the images or on predefined models of the image content and use the image itself as the feature. A novel tracking algorithm is introduced and applied to hand and head tracking. The tracked body parts are used in order to calculate additional features to improve recognition performance. The presented automatic sign language recognition system is evaluated on a set of sentences in American Sign Language.
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