IRJET- Text Recognization of Product for Blind Person using MATLAB

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 12 | Dec 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1048
Text Recognization of Product for Blind Person using MATLAB
Rupali Deshmukh1, Prof. Manoj Kathane2, Prof. Yogesh Sushir3
1Student of M.E. E&TC of Dr. V. B. Kolte College of Engg. Malkapur
2,3Professor, E&TC of Dr. V. B. Kolte College of Engg. Malkapur
----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – There is printed text everywhere around us and
we see it in our day to day life. Like product names, restaurant
menus, instructions on medicines etc. But the question arises
how Visually Impaired or blind people can recognize this text.
Thus surely they need some assistance to read the text. In this
project I tried to propose a camera-based assistive text
reading framework to help low visualpowerorblindpersonto
read text and product label or document from hand-held
objects. By using MATLAB coding and cameraitistriedtohelp
blind persons to read information of the products. In this
project camera acts as main vision in detecting the label
image of the product then image is processed internally and
separates label from image by using MATLAB program and
finally identifies the product nameand informationtheoptical
character recognition.
Key Words: Text reorganisation, camera-based text
assistance, MATLAB algorithm for RGB
1. INTRODUCTION
Recent developments in computer systems,digital cameras,
and different software like MATLABmakeitfeasibletoassist
low visibility individuals by developing camera-based
products that combine computer vision technology with
other existing commercial products such optical character
recognition (OCR) systems. Million people are visually
impaired worldwide, near about 39 millions are blind. Even
in developing country like India, in 2015 Blind people
Association survey reported that a 12 million people are
blind. Using system like video magnifiers, screen readers
help blind person and those with low vision to access the
documents and text. The ability of people who are blind or
have low visual impairments to read printed labels and
documents will enhance independent living and social self-
sufficiency. Today, there are many systems that have
promise to portable use, but theycannot providetheproduct
labeling. Such systems are bar code reader which helps to
blind person to identify the different product. Database can
gives the permission to access the information for blind
persons about the product through speech .But there is big
limitation for blind person to find the position of barcode on
the product. Some assistive systemslikepanescannerwhich
is used in some situations. Suchsystemsintegrated with OCR
software having functions to scanning and recognitionofthe
text and have integrated voice output. These systems
generally design to read the text from simple backgrounds,
standard fonts and also small range of fonts. Some systems
need only white background for scanning the text. This
system cannot read the text from the complex background.
Reading is essential for every human being. Printed text is
everywhere in theformof reports,receipts,bank statements,
restaurant menus, product packages, medicine bottles etc.
can help blind users and those with low vision to read text,
there are few devices that can provide good access to
common hand-held objects such as product packages, and
object sprinted with text such as prescription medication
bottles. The ability of people who are blind or have
significant visual impairments to read printed labels and
product packages will enhanceindependentlivingandfoster
economic and social self-sufficiency. Image processing is
processing of images using mathematical operations in any
form of signal processing for which the input is an image.
The output of image processing may be either an image or a
set of characteristics or the parameters which is related to
the image. Most image-processing techniques whichinvolve
treating the image as a two-dimensional signal and applying
the standard signal-processing techniques to the input.
Image processing is usually refers to digital image
processing, optical and analog image processing also are
possible. The acquisition of images is referred to as imaging.
The close related to image processing is computer graphics
and computer vision. In computer graphics, images are
manually made fromphysical modelsofobjects,surrounding
and lighting, instead of being acquired from natural scenes,
and also in most animated movies. Computer vision, on the
other hand is often considereda high-level imageprocessing.
In modern sciences and technologies, images also get much
broader scopes due to the ever growing importance of
scientific visualization .The millions of visually impaired
people in worldwide are still blind.
1.1 Problem Review
For visually Impaired or blind people it is very important
to be an independent. For that itisimportanttoprovidesome
assistance them in reading. So, I tried to propose a camera-
based assistive text reading framework to help low visual
power or blind person to read text and product label or
document from hand-held objects.
1.2 Objective
Our Objective is to develop a system for visually challenged
person who can help them to identify the various products
and give some more information about the product. To fulfill
this objective some sub objectives were formed which are as
following.

i) Identify the common deficiency in most of the character
recognitionsoftware/toolsbycalculatingtherecognitionrate
of each character and digit and find out the characters and
digits whose recognition rate is very less.
ii) Designing and development of the model to eliminate the
common deficiency identified.
iii) Develop the algorithm to implement the above model.
iv) Testing and Performance evaluation by analyzing results
of model
1.3 CONTRIBUTION
The algorithm used previously cannot handle complex
background and multiple patterns, and extract text
information from hand-held objects. In assistive reading
systems for blind persons, it is very challenging for users to
position the region of interest within the center of the
camera’s view. As of now, there are still no acceptable
solutions.
In this project the previous drawback of algorithm can be
minimized and divided the problem in stages. To make sure
the hand-held object appears in the camera view, a camera
with sufficiently wide angle to accommodate users with only
approximate aim. This may often result in other text objects
appearing in the camera’s view. To extract the hand-held
object from the camera image, a motion-based method to
obtain a region of interest of the object is used.
It is a challenging problem to automatically localize objects
and text ROIs from captured images with complex
backgrounds, because text in captured images is most likely
surrounded by various background outlier “noise,” and text
characters usuallyappearinmultiplescales,fonts,andcolors.
For the text orientations, algorithm used in the previous
paper assumes that text strings in scene images keep
approximately horizontal alignment but that drawback of
algorithm will overcome by algorithm which is best suitable.
Many algorithms have been developed for localizationoftext
regions in scene images. So we will be working on vertical
character recognition problem and try to solve the problem
by using Image Processing.
2. THEORETICAL BACKGROUND
2.1 Image Input
The Image or we can say printed text/label is captured by
camera which is used in project. Initially this image
containing noise in background. This complicated
background can be removed by stroke width transform
algorithm that helps torecognizethecharacterbytheirshape
and width by calculating eachpixel bytheirstarttoendpoint.
2.2 Conversion of RGB to Gray Scale
To make the system more simple i.e. work for noisy
conditions orcomplicatedbackground,imagepre-processing
methods like noise filtering are applied. The processing time
of the overall process islong,so to reduce this processtiming
the input image is converted from RGB to gray scale. This
preprocessing of images in this paper is a technique to
improve the quality of images. The main purpose of this
conversion is to enhance and extracts useful information
from the image. Two preprocessing tasks, thresholding and
noise removal, are performed here.
2.3 Text Binarization
There are numbers of methods for binarization in document
analysis but few in text analysis. In this paper, we reviewed
text analysis binarization methods related. Thresholding
techniques are quite popular in document analysis. Several
improvements over thresholding techniques are also
proposed recently in document analysis and people try the
same methods to extend for scene text binarization also.
2.4 Filter image
It is nothing but the image processing,sincethemethodstake
an input image and create another image as output. Other
appropriate terms often used are ltering, enhancement, or
conditioning. The major notion is that the image contains
some signal or structure, which we want to extract, along
with uninteresting or unwanted variation, which we want to
suppress. If decisions are made about the image, they are
made at the level of a single pixel or its local neighborhood.
2.5 Automatic Text Extraction
There are two popular methods forextractingatextregionin
images are, merge and split method and comparison of two
frames. Thus, they take long computing timeduetotheuseof
a whole image. So automatic text extraction algorithm is
implemented to detect the region containing thelabeltext.In
order to handle complex backgrounds, two novel feature
maps to extracts text features based on stroke orientations
and edge distributions, respectively are used. Maximally
stable external region is used in automatic text extraction.
2.6 Optical Character Recognition
Text recognition is performed by off-the-shelf OCR prior to
output of informativewordsfromthelocalizedtextregions.A
text region labels the minimum rectangular area for the
accommodation of characters inside it, so the border of the
text region contacts the edge boundary of the text character.
However, OCR generates better performance if text regions
are first assigned proper margin areas and binarized to
segment text charactersfrombackground.Weproposetouse

Templatematchingalgorithm for OCR. TheoutputoftheOCR
is nothing but a text file containing the product label (its
name) in textual form. Audio output component is to inform
the blind user of recognize text code in the form of speech or
Audio.
3. Algorithm used for Image to Audio Conversion
3.1 Text Detection Algorithm
We describe the text detection algorithm that is MSER
(Maximally Stable Extremal Region) algorithm. MSER is a
method for text detection, blob detection in images. The
MSER algorithm extracts number of co-variant regions from
image. We rst de ne the concept of stroke and then explain
the stroke width transformation.MSERisbasedontheideaof
taking regions which stay nearly the same through a wide
range of thresholds. All the pixels above or equal to a given
threshold are black and all the pixels below a giventhreshold
are white. MSER uses two important properties to remove
non text regions from image rst is Geometric Properties and
another is Stroke Width Variation Properties.
3.2 Geometric properties
MSER detects almost all text regions from imagebut
alongside it also detect some non-text regions. To remove
those non text regions rst we apply Geometric Properties on
image. Geometric Properties detects the non-text regions
from image and remove those regions.
Fig. Flow chart of the Proposed Method
3.3 Stroke Width Transform
It receives the RGB image with the help of algorithm
the image is converted into grey but of the same size after
that text can be marked from the region of interest. It has
three important stages: first the most important stage is
stroke width transform, then collection of pixel of images on
their stroke width, then pigeonholing letter candidates into
regions of text. In Stroke Width Transform, the stroke in
image is converted into constant width with the help of
continuous band. Figure shows example of stroke in image.
The Stroke Width Transform is operation for calculating
width of pixel stroke from image.
4. Result and Discussion
Fig. OCR based product name Recognition System
Here, two images have been taken one is of Dove and one of
Battery. Toggle button 1 this button is used for Character
recognition and Character Identification.
Fig. MSER Region
Fig. Region and stroke width image
As shown in the Result figure the toggle button 1 Proceed
Character Recognition by using OCR. This image of result
shows the region AfterRemovingNon-TextRegionsBasedon
Geometric Properties OCR is the stand of optical character
recognition which is field of computer science that
recognizing image-based text from photos and transforms it
to real digital character. OCR works like human ability in the
brain to recognize the letters,numbersandsymbols.OCRcan
read both handwritten and printed text. The performance of
OCR is directly related to quality of input documents and
pictures.

Fig. Expanded Bounding Boxes Text
In this it will find out all the Region of Interest. And it will
identify and find out its Stroke Width of Image. Stroke Width
is a measure of the curves and lines that make up character.
Text regions have little stroke width variation, whereas non
text regions have larger variations. To remove the non-text
regions using stroke width we require thresholds. All the
pixels above or equal to a given threshold are black and all
the pixels below a given threshold are white.
5. Summary and Conclusion
In this project, we proposed algorithm for solving the
problem of character recognition for blind persons. We had
given the input in the form of images of a product. The
algorithm was trained on the training data that was initially
present in the database. We have done pre-processing and
OCR classification and detect the text. The project presents a
brief survey of the applications in various fields along with
experimentation into few selected fields. The proposed
method is extremely efficient to extract all kinds of products
which have text including blur and illumination. The project
includes two different methods of product identification
which are successfully implemented.
The text-image recognition initiatives discussed in the
preceding sections illustrate research themes at OCR which
expand and redefine the role of recognition technology in
document-oriented applications. These include the
development of editors which operate directly on scanned
image data, and the use of text-image recognition to retrieve
text information from documents with content.Keyconcepts
embodied in these research efforts include partial document
models, task-oriented document recognition, user
specification and interpretation of recognition models, and
automatic generationofrecognizersfromdeclarativemodels.
These concepts enable the realization of a broad range of
signal-based documentprocessingoperations,includingfont,
vocabulary andlanguage-independent classificationandtext
detection.
6. Future scope
 future work of this project is to recognize the text
from the more complex background and different
type of challenging background surfaces
 Such system can be implement to read the text from
the object and translate output in different
languages.
 Future work can also extend for localization
algorithm to process text strings with characters
fewer than three and to design more robust block
patterns for text feature extraction.
 We will also extend our algorithm to handle incline
text strings. Furthermore, we will address the
significant human interface issues associated with
reading text by blind users.
ACKNOWLEDGEMENT
I offer sincere and heartily thank, with deep sense of
gratitude to our guideProf. Monoj Kathane andcoguideProf.
Yogesh Sushir for their valuable guidance, direction and
inspiration to my project work without taking care of their
voluminous work. I am also thankful to all teacher for taking
personal Interest, giving encouragement and timely
suggestion and notable guidance.
REFERENCES
[1] Chucai Yi, Student Member, Ieee, Yingli Tian, Senior
Member, Ieee, And Aries Arditi “Portable Camera-Based
Assistive Text And Product Label Reading From Hand-Held
Objects For Blind Persons”, Ieee/Asme Transactions On
Mechatronics, Vol. 19, No. 3, June 2014.
[2] Nagarathna, 2Sowjanya V M, “Product Label Reading
SystemForVisuallyChallengedPeople”,InternationalJournal
Of Computer Science And Information Technology Research
Issn 2348-120x (Online) Vol. 3, Issue 2, Pp: (897-904),
Month: April - June 2015.
[3] Ms Komal Mohan Kalbhor1, Mr Kale S.D.2, “ A Survey On
Portable Camera-Based Assistive Text And Product Label
Reading From Hand-Held Objects For Blind Persons”,
International Research Journal Of Engineering And
Technology (Irjet), Volume: 04 Issue: 03 Mar -2017
[4] Priyanka Patil1, Sonali Solat2, Shital Hake Prof.S.T.Khot4,
“Camera Based Product Information Reading For Blind
People”, International Journal Of Engineering And Computer
Science Issn:2319-7242 Volume 4 Issue 3 March 2015, Page
No. 11072-11075

IRJET- Text Recognization of Product for Blind Person using MATLAB

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