Toward Scalability in ASL Recognition: Breaking Down Signs into Phonemes
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
A Real-Time Continuous Gesture Recognition System for Sign Language
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Novel boosting framework for subunit-based sign language recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Static gesture quantization and DCT based sign language generation
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
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Up to now analytical or statistical methods have been used in sign language recognition with large vocabulary. Analytical methods such as Dynamic Time Wrapping (DTW) or Euclidian distance have been used for isolated word recognition, but the performance is not satisfactory enough because it is easily interfered by noise. Statistical methods, especially hidden Markov Models are commonly used, for both continuous sign language and isolated words and with the expansion of vocabulary the processing time becomes increasingly unacceptable. Therefore, a multilayer architecture of sign language recognition for large vocabulary is proposed in this paper for the purpose of speeding up the recognition process. In this method the gesture sequence to be recognized is first located at a set of words that are easy to be confused (confusion set) through a global cursory search and then the gesture is recognized through a latter local search and the generation of confusion set is realized by DTW/ISODATA algorithm. Experiment results indicate that it is an effective algorithm for Chinese sign language recognition.