Fingerspelling recognition through classification of letter-to-letter transitions

Published

Journal Article

We propose a new principle for recognizing .ngerspelling 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. © Springer-Verlag 2010.

Full Text

Duke Authors

Cited Authors

  • Ricco, S; Tomasi, C

Published Date

  • December 29, 2010

Published In

Volume / Issue

  • 5996 LNCS / PART 3

Start / End Page

  • 214 - 225

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-12297-2_21

Citation Source

  • Scopus