This document presents a sign language translation software developed using LABVIEW. The software can translate one-handed Indian sign language representations of alphabets and numbers to spoken English as well as translate 165 spoken English words to sign language gestures. It uses computer vision techniques to capture sign language input via a webcam and recognize gestures through pattern matching with stored templates. For speech input and output, it interfaces with the computer's microphone and speakers. The software provides a graphical user interface to demonstrate sign to speech and speech to sign translation. While accurate for the limited vocabulary trained, the system's accuracy may decrease for similar signs and would benefit from a larger more varied training database.

1. Automatic Language Translation Software For Aiding Communication Between Indian Sign Language And Spoken
English Using Labview
Yellapu Madhuri*, G.Anitha**
* 2nd year M.Tech, ** Assistant Professor
Department of Biomedical Engineering,
SRM University, Kattankulathur-603203, Tamilnadu, India
www.srmuniv.ac.in
Sign Language (SL) is the natural way of communication of speech and/or
hearing-impaired people. A sign is a movement of one or both hands,
accompanied with facial expression, which corresponds to a specific meaning.
This paper presents SIGN LANGUAGE TRANSLATION software for
automatic translation of Indian sign language into spoken English and vice
versa to assist the communication between speech and/or hearing impaired
people and hearing people. It could be used by deaf community as a translator
to people that do not understand sign language, avoiding by this way the
intervention of an intermediate person for interpretation and allow
communication using their natural way of speaking. The proposed software is
standalone executable interactive application program developed using
LABVIEW software that can be implemented in any standard windows
operating laptop, desktop or an IOS mobile phone to operate with the camera,
processor and audio device. For sign to speech translation, the one handed SL
gestures of the user are captured using camera; vision analysis functions are
performed in the operating system and provide corresponding speech output
through audio device. For speech to SL translation the speech input of the user
is acquired by microphone; speech analysis functions are performed and
provide SL gesture picture display of corresponding speech input. The
experienced lag time for translation is little because of parallel processing and
allows for instantaneous translation from finger and hand movements to speech
and speech inputs to SL gestures. This system is trained to translate one handed
SL representations of alphabets (A-Z), numbers (1-9) to speech and 165 word
phrases to SL gestures The training database of inputs can be easily extended to
expand the system applications. The software does not require the user to use
any special hand gloves. The results are found to be highly consistent,
reproducible, with fairly high precision and accuracy.
AIM :
To develop a mobile interactive application program for automatic
translation of Indian sign language into spoken English and vice-versa to assist
the communication between Deaf people and hearing people. The SL translator
should be able to translate one handed Indian Sign language finger spelling input
of alphabets (A-Z) and numbers (1-9) to spoken English audio output and 165
spoken English word input to Indian Sign language picture display output.
OBJECTIVES:
•To acquire one handed SL finger spelling of alphabets (A to Z) and numbers (1
to 9) to produce spoken English audio output.
•To acquire spoken English word input to produce Indian Sign language picture
display output.
•To create an executable file to make the software a standalone application.
•To implement the software and optimize the parameters to improve the accuracy
of translation.
•To minimize hardware requirements and thus expense while achieving high
precision of translation.
There is a need for monitoring cerebral perfusion
MATERIALS
Software Tools used: National Instruments
LabVIEW and toolkits
•LABVIEW 2012 version
•Vision Development Module
•Vision acquisition module
Hardware tools used
•Laptop inbuilt webcamera- Acer Crystal Eye
•Laptop inbuilt speaker-Acer eAudio
METHOD:
The software is a standalone application. To install the file, follow the
instructions that appear in the executable installer file. After installing the
application, a Graphical user interfacing (GUI) window opens, from which the
full application can be used. The GUI has been created to run the entire
application from a single window. It has four pages, each page corresponds to a
specific application.
PAGE 1 gives a detailed demo of the total software usage.
PAGE 2 is for speech to sign language translation.
When the “start” button is pressed, a command is sent to the Windows 7 inbuilt
Speech Recognizer and it opens a mini window at the top. The first time it is
started, a tutorial session begins which gives instructions to setup the microphone
and recognize the user’s voice input. Configure the speech recognition software.
After the initial training, from the next time the program is executed, it starts
speech recognition automatically. To train the system for a different user or
change the microphone settings, right click on the Speech Recognizer window
and select “Start Speech Tutorial”. To stop the speech recognition software say
“Stop listening”. To start speech recognition again say “Start Listening”. When
the user utters any of the words listed in the “Phrases” it is displayed in the
“Command” indicator. A SL gesture picture corresponding to the speech input is
displayed in the “Sign” picture indicator. The score of speech input correlation
with the trained word is displayed in the “Score” numeric indicator. Use the exit
button to exit the application of speech to SL translation.
PAGE 3 is for template preparation for sign to speech translation.
To execute the template preparation module, press the “Start” button.
Choose the camera to acquire images to be used as templates, from the “Camera
Name” list. The acquired image is displayed on “Image” picture indicator. If the
display image is good to be used for preparing a template, press “Snap frame”.
The snapped image is displayed on “Snap Image” picture display. Draw a region
of interest to prepare the template and press “Learn”. The image region in the
selected portion of the snapped frame is saved to the folder specified for
templates. The saved template image is displayed on “Template Image” picture
display. Press “Stop” button to stop execution of template preparation module.
PAGE 4 is for Sign to speech translation.
Press the “Start” button to start the program. Choose the camera to acquire
images to be used for pattern matching, from the “Camera Name” list. The
captured images are displayed on the “Input Image” picture display. Press the
“Match” button to start comparing the acquired input image with the template
images in the data base. In each iteration the input image is checked for pattern
match with one template. When the input image matches with the template image,
the loop halts. The “Match” LED glows and the matched template is displayed on
the “Template Image” indicator. The loop iteration count is used for triggering a
case structure. Depending on the iteration count value a specific case is selected
and gives a string output. Otherwise the loop continues to next iteration where the
input image is checked for pattern match with a new template. The information
in the string output from case structure is displayed on the “Matched Pattern”
alphanumeric indicator. It also initiates the .NET speech synthesizer to give an
audio output through the speaker.
Figure 1.1 Events involved in hearing Figure 1.2 Speech chain
Figure 1.3 Graphical Abstract

2. [1]. Yellapu Madhuri, G.Anitha (2013) “VISION-BASED SIGN
LANGUAGE TRANSLATION DEVICE” International Conference on
Information Communication & Embedded systems ICICES 2013 in association
with IEEE, S.A engineering College, Chennai. ISBN No. 978-1-4673-5787-6G.
Tracking Id: 13cse213.
[2]. Yellapu Madhuri, G.Anitha (2013) “Automatic Language Translation
Software for Interpreting Sign Language and Speech in English”, has been
awarded Silver medal in paper presentation Research Day 2013 at SRM
University, Chennai.
[3]. Yellapu Madhuri, G.Anitha (2013) submission entitled "SIGN
LANGUAGE TRANSLATOR" has been assigned the following manuscript.
number: IMAVIS-D-13-00011 by Elsevier Editorial Systems- Image and
Vision Computing journal imavis@elsevier.com.
[4]. Yellapu Madhuri, G.Anitha (2013) submission entitled “VISION-BASED
SIGN LANGUAGE TRANSLATOR” is Accepted for publication
in International Journal of Engineering and Science Invention (IJESI)
www.ijesi.org.Review report of manuscript id: A11023.
[5]. Yellapu Madhuri, G.Anitha (2013) submission entitled “SIGN
LANGUAGE TRANSLATION DEVICE” is Accepted for publication
in The International Journal of Engineering and Science (THE IJES)
www.theijes.com. Review report of manuscript id: 13026.
[6]. Yellapu Madhuri, G.Anitha (2013) submission entitled "Automatic
Language Translation Software for Interpreting Sign Language and Speech in
English" has been assigned a tracking number of NCOMMS-13-02048 by
Nature Communications naturecommunications@nature.com.
[1]. Jose L. Hernandez-Rebollar1, Nicholas Kyriakopoulos1, Robert W.
Lindeman2 ‘A New Instrumented Approach For Translating American
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(FGR’04) 0-7695-2122-3/04 $ 20.00 © 2004 IEEE.
[2]. K. Abe, H. Saito, S. Ozawa: Virtual 3D Interface System via Hand
Motion Recognition From Two Cameras. IEEE Trans. Systems, Man,
and Cybernetics, Vol. 32, No. 4, pp. 536–540, July 2002.
[3]. Paschaloudi N. Vassilia, Margaritis G. Konstantinos "Listening to
deaf': A Greek sign language translator’, 0-7803-9521-2/06/$20.00
§2006IEEE
Name: YELLAPU MADHURI
Reg.No:1651110002
M.Tech (BiomedialEngineering)
Mobile no: 09441571241
E.Mail:ymadhury@rediffmail.com
In this work, a vision based sign language recognition system using LABVIEW for
automatic sign language translation has been presented. This approach uses the
feature vectors which include whole image frames containing all the aspects of the
sign. This project has investigated the different issues of this new approach to SL
recognition to recognize on the hand sign language alphabets and numbers using
appearance based features which are extracted directly from a video stream recorded
with a conventional camera making recognition system more practical. Although
sign language contains many different aspects from manual and non-manual cues,
the position, the orientation and the configuration or shape of the dominant hand of
the signer conveys a large portion of the information of the signs. Therefore, the
geometric features which are extracted from the signers’ dominant hand, improve
the accuracy of the system to a great degree. This project did not focus on facial
expressions although it is well known that facial expressions convey important part
of sign-languages. The facial expressions can e.g. be extracted by tracking the
signers’ face. Then, the most discriminative features can be selected by employing a
dimensionality reduction method and this cue could also be fused into the
recognition system.
The sign language translator is able to translate alphabets (A-Z) and
numbers (1-9). All the signs can be translated real-time. But signs that are
similar in posture and gesture to another sign can be misinterpreted,
resulting in a decrease in accuracy of the system. The current system has
only been trained on a very small database. Since there will always be
variation in either the signers hand posture or motion trajectory, a larger
database accommodating a larger variety of hand posture for each sign is
required. The speech recognition program requires the user to take up a
tutorial of 10 minutes. During the training, the program learns the accent of
the user for speech recognition. It is observed that, the longer the user used
the program , the higher the accuracy of speech recognition.
This paper presents a novel approach for gesture detection. This approach
has two main steps: i) template preparation, and ii) gesture detection. The
template preparation technique presented here has some important features
for gesture recognition including robustness against slight rotation, small
number of required features and device independence. For gesture detection,
a pattern matching technique is used. The gesture recognition technique
presented here can be used with a variety of front-end input systems such as
vision based input , hand and eye tracking, digital tablet, mouse, and digital
glove. Much previous work has focused on isolated sign language
recognition with clear pauses after each sign. These pauses make it a much
easier problem than continuous recognition without pauses between the
individual signs, because explicit segmentation of a continuous input stream
into the individual signs is very difficult. For this reason, and because of co-
articulation effects, work on isolated recognition often does not generalize
easily to continuous recognition. But the proposed software captures the
input images as an AVI sequence of continuous images. This allows for
continuous input image acquisition without pauses. But each image frame is
processed individually and checked for pattern matching. This technique
overcomes the problem of processing continuous images at the same time
having input stream without pauses.
For Speech to SL translation words of similar pronunciation are
sometimes misinterpreted. This problem can be avoided by clearly
pronouncing the words and with extended training and increasing usage. The
speech recognition technique introduced in this article can be used with a
variety of front-end input systems such as computer and video games,
precision surgery, domestic applications and wearable computers.
Figure 1.4 Block diagram of SL Figure 1.5 Block diagram of Speech
to speech translation to sign language translation
Figure 1.6 PAGE 3-GUI of template
preparation
Figure 1.8 PAGE 2-GUI of Speech to
SL translation
Figure 1.9 GUI of windows
speech recognition tutorial
Figure 1.7 PAGE 4-GUI of SL to
speech translation
Figure 1.10 Database of SL finger spelling Alphabets and Numbers