Date of Publication

2008

Document Type

Master's Thesis

Degree Name

Master of Science in Computer Science

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Nelson Marcos

Defense Panel Chair

Rachel O. Roxas

Defense Panel Member

Joel P. Ilao
Nelson Marcos

Abstract/Summary

Sign language number recognition system lays down foundation for handshape recognition which addresses real and current problems in signing in the deaf community and leads to practical applications. The input for the sign language number recognition system is Filipino Sign Language number video files. The study is limited to include only 1000 numbers in Filipino Sign Language from number 1 to 1000. Each number is recorded 5 times using web camera. The frame size of the video is 640 x 480 and the speed is 15 frames per second. A student from School of Deaf Education and Applied Studies (SDEAS) De La Salle-College of Saint Benilde (DLS-CSB) does the Filipino Sign Language numbers with color-coded glove for dominant hand. The color coded gloves uses less color compared with other color-coded gloves in the existing research. The system extracts important features from the video using multi-color tracking algorithm which is faster than existing color tracking algorithm because it did not use recursive technique. The feature vectors contain the position of dominant-hands thumb in x and y coordinates and the x and y coordinates of other fingers relatively to the thumb position. Next, the system learns the Filipino Sign Language number in training phase and recognizes the Filipino Sign Language number in testing phase by transcribing Filipino Sign Language number into text. The system uses Hidden Markov Model (HMM) for training and testing phase. The system was evaluated in terms of training time and accuracy. The feature extraction could track 92.3% of all objects. The recognizer also could recognize Filipino sign language number with 85.52% average accuracy using the features from feature extraction module. Keywords Computer vision, Human Computer Interaction (HCI), Sign Language Recognition (SLR), Hidden Markov Model (HMM), hand tracking, multi-color tracking.

Abstract Format

html

Language

English

Format

Electronic

Accession Number

CDTG004556

Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

viii, 122 leaves ; 28 cm.

Keywords

Human-computer interaction; Computer vision; Neural networks (Computer science); Hidden Markov models

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