scholarly journals American Sign Language to Text - Speech using Background Subtraction using Running Averages

2019 ◽  
Vol 9 (2) ◽  
pp. 2150-2156
Keyword(s):  
Feature Extraction ◽  
American Sign Language ◽  
Sign Language ◽  
Web Applications ◽  
Background Model ◽  
Absolute Difference ◽  
American Sign ◽  
Current Frame ◽  
Proposed Model ◽  
Standard Techniques

This Paper Proposes A System Which Converts American Sign Language Hand Gestures Into Text Cum Speech And Helps To Bridge The Communication Gap Between DeafMute People And Rest Of The Society. Any System For This Purpose Generally Has Four Modules: Segmentation, Feature Extraction, Classification And Text-To-Speech. This Paper Focuses On An Improved Method For The Segmentation And The Feature Extraction Processes To Get More Better Resultswhile Using The Standard Techniques On The Other Two Modules. Proposed Algorithm Captures Initial 30 Frames Of The Live Video From The Web Cam Of The System To Construct The Background Model. It Then Finds The Absolute Difference Between The Current Frame And The Background Model In Order To Get The Foreground. Various Features Are Extracted To Classify The Gestures Like Contour, Convexity Hull Etc.. Proposed Algorithm Has Been Tested Under Low And Normal Room Light Conditions. The Overall Performance Of The Proposed Model Will Be Very High And Will Produce Far More Better Resultsdue To Improved Proposed Algorithms For The Initial Two Modules In Comparison To Other Standard Techniques Used Like Hsv, Ycbcr The Above System Can Be Incorporated Into Simple Web Applications, Mobile Applications And Many Other Applications Translating Gestures In The Conversations In Real Time.

scholarly journals A Bag-of-Words Based Feature Extraction Scheme for American Sign Language Number Recognition from Hand Gesture Images

2021 ◽  
Vol 11 (1) ◽  
pp. 85-91
Author(s):  
Rasel Ahmed Bhuiyan ◽  
◽  
Abdul Matin ◽  
Md. Shafiur Raihan Shafi ◽  
Amit Kumar Kundu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
American Sign Language ◽  
Sign Language ◽  
Superior Performance ◽  
Hand Gesture ◽  
Bag Of Words ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Number Recognition

Human Computer Interaction (HCI) focuses on the interaction between humans and machines. An extensive list of applications exists for hand gesture recognition techniques, major candidates for HCI. The list covers various fields, one of which is sign language recognition. In this field, however, high accuracy and robustness are both needed; both present a major challenge. In addition, feature extraction from hand gesture images is a tough task because of the many parameters associated with them. This paper proposes an approach based on a bag-of-words (BoW) model for automatic recognition of American Sign Language (ASL) numbers. In this method, the first step is to obtain the set of representative vocabularies by applying a K-means clustering algorithm to a few randomly chosen images. Next, the vocabularies are used as bin centers for BoW histogram construction. The proposed histograms are shown to provide distinguishable features for classification of ASL numbers. For the purpose of classification, the K-nearest neighbors (kNN) classifier is employed utilizing the BoW histogram bin frequencies as features. For validation, very large experiments are done on two large ASL number-recognition datasets; the proposed method shows superior performance in classifying the numbers, achieving an F1 score of 99.92% in the Kaggle ASL numbers dataset.

scholarly journals American Sign Language Alphabet Translator Android Application: Hand Shapes Into Text

2015 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Love Jhoye M. Raboy ◽  
Jan Rey D. Canlas ◽  
Christy Ann R. Renejane ◽  
Carren J. Mojica ◽  
Annajane S. Bandiala
Keyword(s):  
Feature Extraction ◽  
American Sign Language ◽  
Sign Language ◽  
Mobile Application ◽  
American Sign ◽  
Android Application ◽  
Data Set ◽  
Hand Segmentation ◽  
Hand Pose

American Sign Language is used by the deaf-mute community in expressing their thoughts. Many applications have been developed to assist the deaf-mute person, but most of the applications integrate on a computer or on desktop. These leads to create a mobile application that will recognize hand pose into text. American Sign Language Alphabet Translator Android Application: Hand Shapes into Text is a mobile application that translates American Sign Language Alphabet into text. These applications translate the ASL alphabet to a person who is not capable of understanding the language of a deaf-mute person. The application used the process of hand segmentation and feature extraction to get the information needed to extract the hand and data set that used to match the hand pose to display the equivalent letters. The study is successful in recognizing most of the alphabet handshape as long as it is well detected.

Spatial and temporal dynamics of lexico-semantic processing in American Sign Language

2011 ◽  
Author(s):  
M. Leonard ◽  
N. Ferjan Ramirez ◽  
C. Torres ◽  
M. Hatrak ◽  
R. Mayberry ◽  
...  
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Semantic Processing ◽  
Temporal Dynamics ◽  
American Sign ◽  
Spatial And Temporal Dynamics

Patient Health Questionnaire-9--American Sign Language Version

2018 ◽  
Author(s):  
Leslie Pertz ◽  
Missy Plegue ◽  
Kathleen Diehl ◽  
Philip Zazove ◽  
Michael McKee
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Patient Health Questionnaire ◽  
American Sign ◽  
Health Questionnaire ◽  
Language Version ◽  
Patient Health
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