Sign Language Recognition By Image Analysis

Date

2016-05-19

Author

Buyuksarac, Buket
Bulut, Mehmet Mete
Akar, Gözde

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

155
views

0
downloads

The Sign Language Recognition (SLR) Problem is a highly important research topic, because of its ability to increase the interaction between the people who are hearing-impaired or impediment in speech. We propose a simple but robust system. The proposed system consists of three main steps. First we apply segmentation to the face and hand region by using Fuzzy C-Means Clustering (FCM) and Thresholding. FCM is a clustering technique which employs fuzzy partitioning, in an iterative algorithm. After the face and hands are segmented, the feature vectors are extracted. The feature vectors are chosen among the low level features such as the bounding ellipse, bounding box, and center of mass coordinates, since they are known to be more robust to segmentation errors due to low resolution images. In total there are 23 features for each hand. After the feature vectors are extracted, they are used for recognition with discrete Hidden Markov Model (HMM). Recognition stage is composed of two stages, namely training and classification. The Baum Welch algorithm is used for HMM training. In classification part the likelihood of each HMM is calculated and the HMM with the highest likelihood is chosen. In order to measure the success rate of the system, the eNTERFACE dataset is used. In this dataset 8 different American Sign Language example classified and in user independent case, is shown to be working with 94.19% accuracy.

Subject Keywords

Sign Language Recognition, Baum Welch, Clustering, Fuzzy C-Means, Discrete Hidden Markov Model, Machine Learning, Machine Vision

URI

https://hdl.handle.net/11511/54120

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Sign language recognition by image analysis / Büyüksaraç, Buket; Bulut, Mehmet Mete; Akar, Gözde; Department of Electrical and Electronics Engineering (2015) The Sign Language Recognition (SLR) Problem is a highly important research topic, because of its ability to increase the interaction between the people who are hearing-impaired or impediment in speech. However there are several limitations of the existing methods. Most applications need different necessities like making the user wear multi-colored or sensor based gloves or usage of a specific camera. We propose a simple but robust system that can be used without the need of any specific accessories. The pro...
SPEECH DETECTION ON BROADCAST AUDIO Zubari, Unal; Ozan, Ezgi Can; Acar, Banu Oskay; Çiloğlu, Tolga; Esen, Ersin; Ates, Tugrul K.; Onur, Duygu Oskay (2010-08-27) Speech boundary detection contributes to performance of speech based applications such as speech recognition and speaker recognition. Speech boundary detector implemented in this study works on broadcast audio as a pre-processor module of a keyword spotter. Speech boundary detection is handled in 3 steps. At first step, audio data is segmented into homogeneous regions in an unsupervised manner. After an ACTIVITY/NON-ACTIVITY decision is made for each region, ACTIVITY regions are classified as Speech/Non-spe...
A Low cost learning based sign language recognition system Akış, Abdullah Hakan; Akar, Gözde; Department of Electrical and Electronics Engineering (2018) Sign Language Recognition (SLR) is an active area of research due to its important role in Human Computer Interaction (HCI). The aim of this work is to automatically recognize hand gestures consisting of the movement of hand, arm and fingers. To achieve this, we studied two different approaches, namely feature based recognition and Convolutional Neural Networks (CNN) based recognition. The first approach is based on segmentation, feature extraction and classification whereas the second one is based on segme...
Oral Communication Skills Seferoğlu, Gölge(2009) This course offers a variety of different communication-oriented speaking opportunities such as discussions, individual and group presentations and other interactive tasks providing opportunity for students to improve their oral competence by developing effective language use both in formal and informal contexts. It offers extended communicative tasks such as debates, role-plays, individual and group presentations, impromptu speeches and other interactive tasks providing opportunity for students to improve ...
Language learning strategies and self-efficacy beliefs as predictors of english proficiency in a language preparatory school Açıkel, Merih; Çapa Aydın, Yeşim; Department of Educational Sciences (2011) The purpose of this study was to examine the relationship of language learning strategy use and self-efficacy beliefs with language proficiency of the language preparatory school students. Moreover, some demographic characteristics of the participants were analyzed in relation to the proficiency scores of the students. Four hundred eighty nine language preparatory school students from one private university in Ankara were included in the study. Turkish version of Inventory of Strategies for Language Learnin...

Citation Formats

B. Buyuksarac, M. M. Bulut, and G. Akar, “Sign Language Recognition By Image Analysis,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54120.