ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Computer Engineering


Computer Science and Engineering

First Advisor

Vassilis Athitsos


An automated computer vision-based dictionary system for looking up the meaning of signs can be an invaluable tool both for students and native users of sign languages. Students may not know the meaning of a sign they encounter and would like to learn what it is. A native signer knows what it means to them but may be unsure of the equivalent in English. Such a system can return a ranked video list of the most similar signs to a query video and allow the user to browse the video results to find the desired sign and its meaning. This thesis investigates and proposes improvements in large vocabulary sign search systems and culminates in an automated American Sign Language dictionary search system with improved accuracy over former variants. This type of dictionary system presents several challenges. When a large vocabulary is desired, it is often not feasible to generate a large enough training set to train statistical and machine learning recognition methods that have achieved good accuracy on smaller vocabularies. In this case, exemplar-based methods must be used and improved upon. Secondly, there are large variations in the performance of signs inherent in user-independent systems. Generative statistical methods like Hidden Markov Models can model these variations but may be unusable in such a system due to the insufficient number of training samples required for learning transition probabilities. This thesis makes the following contributions. First, there is a lack of publicly available, fully annotated, large vocabulary RGB-D gesture datasets for use in gesture recognition research. Thus, a multimodal 3D body part detection and large vocabulary American Sign Language dataset is presented that allows researchers to evaluate both body part (i.e. hands and shoulders) detection and gesture recognition methods. This dataset is used to establish benchmarks and for testing the methods developed in this work. The primary differences between this dataset and others are the vocabulary size and the full annotations of joint positions in every frame of each gesture. Second, this thesis proposes Intra-Class Variation Modeling, a method that addresses the wide variability in sign performance by generating models for same-class differences in several geometric properties of the hand trajectories comprising the signs. These models can be used to generate features that describe the likelihood that a query sign matches an example sign given the observed differences in these properties and provide an improvement to the exemplar-based similarity measure. The third contribution of this work is Multiple-Pass Dynamic Time Warping, a way to better handle various size and spatial translation differences in the performance of signs by multiple users. Each DTW pass centers and sizes the sign using a different set of properties to generate multiple scores that can be combined to provide a better measure of similarity. The two methods are evaluated using a vocabulary of 1,113 signs in both user-dependent and more realistic user-independent experiments with fluent signers. While either method alone achieves an improvement in accuracy, particularly on subjects who perform the signs with large variation from the models, the combination of both techniques provides the best and most significant results. Finally, an improvement in accuracy is demonstrated on actual users of the dictionary system, who are unfamiliar with American Sign Language.


Gesture recognition, Sign language recognition, Kinect


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington