International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 5- May 2015
Ms. S. Umadevi
, Mrs. S. Sebija
PG student,
Assistant Professor, Dept. of Biomedical Engineering, Anna University,
Udaya School of Engineering, Ammandivilai, Kanyakumari, Tamil Nadu, India.
Abstract— The assistive technology is the most important
aspects of visually impaired and for learning disabilities for their
solutions and assist them for their daily life. Assistive Technology
is a generic term incorporating technology, equipment, devices,
appliances, services, systems, processes and environmental
change (Environmental Modifications) used by people with
disabilities or older people to overcome social, infrastructural
barriers, to actively participate in society and to perform
activities easily and safely. From the point of view of visually
impaired people the perception of the surrounding environment
is very important, even essential, in order to facilitate their
mobility. For several unsighted people learning choices are made
based on which matter can be performed and which cannot.
Topmost for numerous in choosing their instructive and
consequent profession pathway is the concept that studying
methodical subjects is complicated due to the extremely
graphical personality of much of the matter offered. In this
paper we have designed an interactive learning tool for visually
impaired learner to convert pictures to sound. In recent years
this process has been widely used for various researches related
to E-learning process for visually impaired peoples. The text to
speech conversion can help the visually impaired person in
understanding content without the help of other persons.
Keywords— Assistive Technology, Text Extraction, Text
Recognition, Visually Impaired Persons, Interactive learning
Assistive technology has removed many barriers
to schooling and service for visually impaired folks.
Students with illustration impairments can complete
training, explore, win tests, and examine books
along with their sighted colleagues. For numerous
unsighted community enlightening choices are
made based on which matter can be accessed and
not. Uppermost for various in choosing their
enlightening and succeeding profession pathway is
the opinion that studying logical subjects is
complicated due to the extremely graphical nature.
ISSN: 2231-5381
In this paper we have designed an interactive
learning tool for visually impaired learner to
convert pictures to sound output. In recent years
this process has been widely used for various
researches interrelated to E-learning progression for
visually impaired peoples. The text to speech
conversion can help the visually impaired person in
understanding substance without the help of other
persons. A system used for such belongings is
called speech synthesizer, and can be implemented
in dissimilar applications.
proficiency offers unsighted and visually impaired
personnel and the capacity to scan the written text
and then converse it reverse in synthetic speech or
keep it to a workstation. Modern technology exists
to understand graphics such as line up fine art,
photograph, and graphs into standard easily
accessible to unsighted and visually impaired
persons. Here the OCR uses three essential
elements of technology such as image scanning,
image identification, and evaluation text. Primarily,
a printed article is scan by a camera. OCR software
then convert the metaphors into humdrum
characters and vocabulary. The speech synthesizer
in the OCR arrangement then speaks the humdrum
text. At last, the sequence is stored in an electronic
form, moreover in a workstation or the recollection
of the OCR mortal itself.
Page 263
International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 5- May 2015
An OCR arrangement will work out that the word
"tke" is a inaccuracy and should be examine as the
word "the." OCR's also use a lexicon and apply for
spell checking techniques similar to many word
processors. All OCR systems create temporary files
containing the texts' characters and page layout.
The blind or visually impaired user accesses the
passage which was scanned by using the adaptive
hi- tech technological campaigns that will blow up
the super computer screen and make accessible
speech are in the form of braille output.
Recognizing text in images is expensive in many
computer visualization applications such as image
search, certificate examination, and machine
navigation. The OCR occupation provides a
straightforward way to insert text recognition
functionality to an extensive assortment of
applications. The OCR functions precede the
recognized text, the recognition self-confidence,
and the position of the text in the innovative
original image. OCR performs best when the text is
located on a uniform background and is formatted
like a document.
The ambition of Optical Character Recognition
(OCR) is to categorize optical patterns in a digital
image) corresponding to alphanumeric or other
typescript. The progression of OCR involves
numerous steps together with segmentation, feature
insertion, and arrangement.
A. Input image
A text-to-speech (TTS) system converts
common language of text, into speech. Synthesized
verbal communication can be formed by
concatenating pieces of recorded vocalizations that
are stored in a catalog. Speech synthesis is applied
in many domains of human life: in language
education, talking books and toys, vocal
monitoring, multimedia communication. High
quality speech synthesis can be used in many others
disciplines: in science, study, industry, aviation, etc.
Here the OCR method is used to extract the text
from the image. The text recognition will examine
every personality and challenge to reconstruct the
imaginative article as concrete wording. Such text
is given to a MATLAB voice tool box.
Recognizing text in images is useful in numerous
computer vision applications. The OCR task
provides a simple technique to contain text
recognition functionality to an extensive choice of
ISSN: 2231-5381
Fig. 1 Original input image with noise
Each of these steps is a turf unto itself, and is
described in a few words here in the context of a
Mat lab implementation of OCR. Optical character
recognition used to extract the text from the input
images by using the method of background
elimination. Background subtraction is preceding
based on intensity of the foreground elements for
the actual text display. The initial text regions are
first detected from the captured image using the
threshold variance of the image and extracted by
using the text extraction in OCR. Image acquired
Page 264
International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 5- May 2015
will be a signage which contains furthermore
alphabet is any alphanumeric characters. The
consumer captures the signage using camera and
put away it in the form of .jpg file. After the
image is captured by the camera the image is
processed and transformed to a text file. The
image taken will go through the development
of adjusting the image contract and intensity
from color representation to make the image
more clearer.
C. Text Extraction
B. Background Eliminated Image
Fig. 3 Extracted Text Output
Fig. 2 Background Elimination
Images are virtually split into small blocks
either in foreground or background component.
Based on intensity variation these classifications of
a foreground or background are done. Intensity
variance of a block is less than an adaptive
threshold it is considered as part of a background.
Otherwise it is considered as a part of foreground
component. This classification is done basis of the
Intensity variation within the block. The intensity is
defined as the difference between Imax and Imin
gray scale intensity within the block.
ISSN: 2231-5381
Analysis of the detected original extracted text
regions by means of simple text detection in a OCR
Technique. By transforming the participation of
information into the arrangement of facial
appearance is called feature extraction. Feature
extraction is the improvement by which image
features are extracted and used to characterize at
duration of the image illustration content. Feature
extraction which involves simplifying the magnitude
of possessions necessary to communicate a
enormous set down of information accurately.
(TTS) Text To Speech synthesis is the nonnatural fabrication of individual communication.
Here the MATLAB tone of voice implement is used
to safeguard the passageway into voice
Page 265
International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 5- May 2015
TTS system arrangement converts a
standard unfamiliar language of wording into a
verbal communication. Such tone of voice
translation will maintain hodgepodge of warping
In our proposed method we have employed an
alternative type for the visually impaired by
developing a learning method where the text
from any sort of images can help them to
understand what is written over the image. The
text from the image is separated and extracted
using the text Detection algorithm then such
extracted text is given to a MATLAB voice
tool to maintain and convert into voice messages.
Then the final voice message directly given to a
The system overcomes the real time difficulties
of visually impaired people, thereby improving
learning ability. System efficiency is
improved with the help of MATLAB image
processing tool. By using this system the blind
educational skill is improved and makes them to
move forward towards the society without any
Fig. 4 MATLAB Voice Message
Thus the proposed system assumes
method of learning process for
visually impaired people. Usually the blind
depend on others for reading or
understanding any information from any
document or from signage images.
ISSN: 2231-5381
Page 266
International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 5- May 2015
Thierry Dutoit., Milos’s Cemak., (2005) “TTS box: A matlab toolbox
for teaching text-ti-speech synthesis”.
environment for non-traditional students with sight disabilities”.
2006 IEEE of 36th ASEE/IEEE frontiers education conference
S.Audithan, RM. Chandrasekaran “Document text extraction from
document images using haar discrete wavelet transform” European
journal of scientific research ISSN 1450216X Vol 36
Ms.R. Senthamarai1, Dr. V. Khanaa. “An efficient method for
visually impaired persons”.
International journal of advanced research (2013), Volume 1, Issue
9, 757-767.
Adesh Kumar, Pankil Ahuja, Rohit Seth “Text extraction and
recognition from an image using image processing in mat lab”.
2013 conference of advances in communication and control system
N.Swetha k.Anuradha “Text-to-speech conversion”. International
journal of research and engineering 2013.
Abhishek Nandy “Pitch detection of speech synthesis by using
mat lab”. International journal of electronics and communication
engineering. Volume 8, issue 1 (sep-2013), PP 42-47.
Mrs. S. D. Suryawanshi, Mrs. R. R. Itkarkar “High quality text to
speech synthesizer using phonetic integration”.
Prof. Amit Choksi1, Nihar Desai “Text extraction from natural
scene images using prewitt edge detection method”. International
journal of advance research and computer science. Vol 03-Issue
12. December 2013.
[10] Dadang Priyanto , Muhamad Nur. “A literacy learning smart system for
the blind children”. International journal of computer and information
technology (ISSN: 2279-0764) Volume 03- issue September 2014.
[11] Reena atul panchal, Rachana papewar “Interactive dictionary for
visually impaired”. IOSR journal of engineering(IOSRJEN).Vol. 04
issue o3 march 2014,PP 42-46.
[12] Piotr porwik, Marek szczepankiewicz “The voice synthesizer of polish
text for blind persons ". Journal of medical informatics and
technologies Vol4/2002,ISSN 1642-6037.
[13] Sándor Tihamér BRASSAI1,Laszlo BAKO2 “ Assistive techniologies
for visually impaired people”. Acta Universitatis Sapientiae Electrical
and Mechanical Engineering, 3 (2011) 39-50.
ISSN: 2231-5381
Page 267
Random flashcards
Arab people

15 Cards


39 Cards


20 Cards


17 Cards

Create flashcards