This document presents a seminar on developing a system to help blind persons read text on product labels using computer vision techniques. The system aims to use a camera-based approach and motion detection methods to define a region of interest containing text. It will then extract and recognize the text to inform blind users via speech or audio output. The document outlines the problem statement, proposed system architecture, potential contributions, timeline, and conclusions. It also reviews several relevant previous studies on assistive text detection and reading technologies for the blind.

1. (Affiliated to Rashtrasant Tukadoji Maharaj Nagpur University)
2015-2016
Presented By
Mr. Vivek R. Chamorshikar
Guided By
Prof. Saiyad Sharik Kaji
P.G. DEPARTMENT OF COMPUTER SCIENCE & ENGG.
WAINGANGA COLLEGE OF ENGINEERING AND MANAGEMENT
NAGPUR.

2. SEMINAR ON
“AN APPROACH FOR TEXT
DETECTION AND READING OF
PRODUCT LABEL FOR BLIND
PERSONS”
Date: 18 / 10 /2015 Seminar Phase: I Seminar No: 1

3. Contents
 Introduction
 Literature Review
 Problem Statement
 System Architecture
 Possible Contribution
 Time Schedule
 Conclusion
 References

4. Introduction
 314 million visually impaired people worldwide, 45
million are blind.
 Camera-based assistive text reading framework to help
blind person read text labels
An efficient and effective motion based method to define a
region of interest (ROI) in the video
Extracted output component is use to inform the blind
user of recognized text codes in the form of speech or
audio

5. Literature Review
No Year of
Publication
Title of the
paper
Author’s
name
Study
1 2014 Portable
Camera-Based
Assistive Text
and Product
Label Reading
From Hand-
Held Objects for
Blind Persons
Chucai Yi,
Yingli Tian and
Aries Arditi
This is the main paper of this
research, this paper totally
focus recent developments in
computer vision, digital
cameras, and portable
computers make it feasible to
assist these individuals by
developing camera-based
products that combine computer
vision technology with other
existing commercial products
such optical character
recognition (OCR) systems.
02 2014 Information and
Assisted
Navigation
System for
Blind People
Karen Duarte,
Jos´e Cec´ılio,
Jorge S´a Silva,
Pedro Furtado
The system presented in this
paper aims to highlight the
user’s device integrating it with
devices and technologies
already used by users, as their
own smartphone.

6. No. Year of
Publication
Title of the
paper
Author’s name Study
03 2009 An algorithm
enabling blind
users to find and
read barcodes
Ender Tekin and
James M.
Coughlan
In this paper, the ability of people who
are blind or have significant visual
impairments to read printed labels and
product packages will enhance
independent living and foster economic
and social self-sufficiency.
04 2007 Text Extraction
and Document
Image
Segmentation
Using Matched
Wavelets and
MRF Model
Sunil Kumar, Rajat
Gupta, Nitin
Khanna, Santanu
Chaudhury and
Shiv Dutt Joshi
This paper proposes scheme for the
extraction of textual areas of an image
using globally matched wavelet filters. A
clustering-based technique has been
devised for estimating globally matched
wavelet filters using a collection of
ground truth images and text extraction
scheme
05 2003 Texture-Based
Approach for
Text Detection in
Images Using
Support Vector
Machines and
Continuously
Adaptive Mean
Shift Algorithm
Kwang In Kim,
Keechul Jung, and
Jin Hyung Kim
This paper show texture-based method
for detecting texts in images. A support
vector machine (SVM) is used to analyze
the textural properties of texts.
No external texture feature extraction
module is used.

7. Problem Statement
 Position the object of interest within the center of the
camera’s view.
 Camera with sufficiently wide angle.
To obtain a region of interest (ROI) of the object with
proper text recognition

8. System Architecture

9. Possible Contribution
To make sure the hand-held object appears in the camera
view.
Obtain a region of interest (ROI) of the object
Motion based algorithm to solve the aiming problem for
blind users
Automatic text localization to extract text regions from
complex background and multiple text patterns

10. Time Schedule
Work to be done /month Jul’
15
Aug’1
5
Sep’15 Oct 15 Nov’15 Dec’15 Jan’ 16 Feb’
16
Mar’
16
Apr’
16
May’
16
June’
16
Studying and analyzing different Data
Stream algorithms and technique.
Studying of literatures regarding Project
Designing of algorithm for the
dynamicity of privacy system
Start implementing Project Phase
I
Phase II
Phase III
Phase IV
Testing
Thesis Preparation

11. Conclusion
This paper has introduced to read printed text on
hand-held objects for assisting blind persons. In order to
solve the common aiming problem for blind users, a
motion-based method to detect the object of interest is
projected, while the blind user simply shakes the object
for a couple of seconds. This method can effectively
distinguish the object of interest from background or
other objects in the camera view.

12. References
[1] Chucai Yi, Yingli Tian 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] Karen Duarte, Jos´e Cec´ýlio, Jorge S´a Silva, Pedro Furtado “Information and
Assisted Navigation System for Blind People”, Proceedings of the 8th International
Conference on Sensing Technology, Sep. 2-4, 2014, Liverpool, UK
[3] Sunil Kumar, Rajat Gupta, Nitin Khanna, Santanu Chaudhury and Shiv Dutt Joshi
“Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF
Model”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 8,
AUGUST 2007
[4] Kwang In Kim, Keechul Jung, and Jin Hyung Kim “Texture-Based Approach for Text
Detection in Images Using Support Vector Machines and Continuously Adaptive Mean
Shift Algorithm”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE
INTELLIGENCE, VOL. 25, NO. 12, DECEMBER 2003
[5] Advance Data Reports from the National Health Interview Survey (2008).[Online].
Available: http://www.cdc.gov/nchs/nhis/nhis_ad.htm.
[6] B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke
width transform,” in Proc. Comput. Vision Pattern Recognition, 2010, pp. 2963–2970.

13. THANKING YOU!