dc.contributor.advisor | Uddin, Jia | |
dc.contributor.author | Arko, Fahmid Nasif | |
dc.contributor.author | Tabassum, Nujhat | |
dc.contributor.author | Trisha, Taposhi Rabeya | |
dc.contributor.author | Ahmed, Fariha | |
dc.date.accessioned | 2017-05-31T06:58:49Z | |
dc.date.available | 2017-05-31T06:58:49Z | |
dc.date.copyright | 2017 | |
dc.date.issued | 4/18/2017 | |
dc.identifier.other | ID 13101087 | |
dc.identifier.other | ID 13101027 | |
dc.identifier.other | ID 13101163 | |
dc.identifier.other | ID 13101024 | |
dc.identifier.uri | http://hdl.handle.net/10361/8216 | |
dc.description | Cataloged from PDF version of thesis report. | |
dc.description | Includes bibliographical references (page 27-29). | |
dc.description | This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. | en_US |
dc.description.abstract | Instant feedback on sign language can greatly improve sign language interpretation. In this project we plan to use efficient methods to detect the hand correctly using skin detection algorithm and removing all noise using MATLAB and classify the image according to the sign gesture performed. In this paper, we propose an image processing based model which will interpret Bangla Sign Language. The purpose of this model is to find Bangla Sign Language recognition accuracy. The model will detect the skin color of every type using 𝑌𝐶𝐵𝐶𝑅 algorithm and use Bag of features for feature extraction and Support Vector Machine (SVM) for training and evaluation. To validate our proposed model, we used our own dataset of Bangla Sign Languages using hand gestures of both male and female. The average accuracy we got from evaluation set is 86%. | en_US |
dc.description.statementofresponsibility | Fahmid Nasif Arko | |
dc.description.statementofresponsibility | Nujhat Tabassum | |
dc.description.statementofresponsibility | Taposhi Rabeya Trisha | |
dc.description.statementofresponsibility | Fariha Ahmed | |
dc.format.extent | 29 pages | |
dc.language.iso | en | en_US |
dc.publisher | BRAC University | en_US |
dc.rights | BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Image processing | en_US |
dc.subject | Sign language | en_US |
dc.title | Bangla sign language interpretation using image processing | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, BRAC University | |
dc.description.degree | B. Computer Science and Engineering | |