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Fingerspelling Recognition through Classification of Letter-to-Letter Transitions

  • Susanna Ricco
  • Carlo Tomasi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5996)

Abstract

We propose a new principle for recognizing fingerspelling sequences from American Sign Language (ASL). Instead of training a system to recognize the static posture for each letter from an isolated frame, we recognize the dynamic gestures corresponding to transitions between letters. This eliminates the need for an explicit temporal segmentation step, which we show is error-prone at speeds used by native signers. We present results from our system recognizing 82 different words signed by a single signer, using more than an hour of training and test video. We demonstrate that recognizing letter-to-letter transitions without temporal segmentation is feasible and results in improved performance.

Keywords

Observation Model American Sign Language Letter Pair Dynamic Gesture Transitional Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Susanna Ricco
    • 1
  • Carlo Tomasi
    • 1
  1. 1.Department of Computer ScienceDuke UniversityDurham

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