Signer-Independent Sign Language Recognition Based on SOFM/HMM

Authors:
Gaolin Fang;Wen Gao;Jiyong Ma
Affiliations:
-;-;-
Venue:
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Year:
2001

Citing 0
Cited 12

Large vocabulary sign language recognition based on hierarchical decision trees

Proceedings of the 5th international conference on Multimodal interfaces
CBIR approach to the recognition of a sign language alphabet

CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
Analyzing the kinematics of bivariate pointing

GI '08 Proceedings of graphics interface 2008
Video-based signer-independent Arabic sign language recognition using hidden Markov models

Applied Soft Computing
An Open Source Framework for Real-Time, Incremental, Static and Dynamic Hand Gesture Learning and Recognition

Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques
Sign Language Recognition: Working with Limited Corpora

UAHCI '09 Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Part III: Applications and Services
A Chinese sign language recognition system based on SOFM/SRN/HMM

Pattern Recognition
Large lexicon detection of sign language

HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Static gesture quantization and DCT based sign language generation

ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Fingerspelling recognition through classification of letter-to-letter transitions

ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Non parametric, self organizing, scalable modeling of spatiotemporal inputs: The sign language paradigm

Neural Networks
Non-manual cues in automatic sign language recognition

Personal and Ubiquitous Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract: The aim of sign language recognition is to provide an efficient and accurate mechanism to transcribe sign language into text or speech. State-of-the-art sign language recognition should be able to solve the signer-independent problem for practical application. In this paper, a hybrid SOFM/HMM system, which combines self-organizing feature maps (SOFMs) with hidden Markov models (HMMs), is presented for signer-independent Chinese Sign Language (CSL) recognition. We implement the SOFM/HMM sign recognition system. Meanwhile, results from the HMM-based system are provided as comparison. Experimental results show the SOFM/HMM system increases the recognition accuracy by 5% than HMM-based one. Furthermore, a self-adjusting recognition algorithm is also proposed for improving the SOFM/HMM discrimination. When it is applied to the SOFM/HMM system it can improve the recognition accuracy by 1.9%. All experiments are performed in real-time with the dictionary size 208.