This presentation is actually about an Open Source application, BrailleOCR that helps to convert scanned documents to Braille and thus helps the Visually Impaired. What is the use of this application in real life? Well, BrailleOCR is currently the only app that integrated Optical character recognition and Braille Translation together. This app will eventually help converting a lot of important documents to Braille. The project site for this project is given here IJCA Paper: http://www.ijcaonline.org/archives/volume68/number16/11664-7254 Project site: https://code.google.com/p/brailleocr/ The app uses a four step process. Initially, we have a scanned image, which is a RGB image. The first step or the Pre-Processing step deals with conversion of a RGB image to grayscale. The 2nd step deals with Character Recognition using the Tesseract Engine. Now, the recognition step may have errors and we require post processing to correct them. The 3rd step is thus the Post-Processing step and it actually corrects errors in the previous step. The final and the most important step is the Braille Conversion step.

1. Calcutta Institute of Engineering and
Management
CS681 Seminar

3.  Contribution of the Application in real life:
o Our application integrates the working of an OCR with
Braille Translation.
o BrailleOCR is currently the only application that supports
conversion of Image document to Braille format.
o Will help in converting large documents to Braille format
and eventually help a lot of Visually Impaired people.
o Project site: code.google.com/p/brailleocr
o DOI IJCA Paper reference: 10.5120/11664-7254
 Open Source APIs used:
o Tesseract Engine[Open-source OCR Engine]
o Tess4J API [JNA Wrapper for using Tesseract with Java]
o JOrtho API [Java open-source spell checking API]
o Swing Graphics API

6.  Conversion of an Image Document to Braille
consists of the following steps:
Fig. 1. Steps to be Followed

7.  Conversion of an Image Document to Braille
consists of the following steps:
Fig. 1. Steps to be Followed

8.  Conversion of an Image Document to Braille
consists of the following steps:
Fig. 1. Steps to be Followed

9.  Conversion of an Image Document to Braille
consists of the following steps:
Fig. 1. Steps to be Followed

11.  Pre Processing Steps:
◦ Conversion to grayscale
◦ Conversion of grayscale image to binary
◦ The second sub-step is handled by Tesseract using
adaptive threshold.
 Reason for Grayscale conversion:
◦ Increases the accuracy in the Recognition step as
stated in Ref. [2].
◦ Table 1 gives the Accuracy rate for certain input
images.
Input Image No. of Images Accuracy
Color Image 10 89%
Grayscale Image 10 93%
Table 1: Accuracy of Tesseract

12.  Different Algorithms available:
 Averaging
 Luminosity method
 Luminosity method Benefits:
 Human perception has more sensitivity for green more that red and red
more than blue
 Wight of green color component is highest followed by red and blue
i.e weight of color channel ∝ sensitivity
 Algorithm Used:
The color image can be represented as a discrete function
f(x,y)=(xi,yj), 0<=i<N, 0<=j<M where N is the height of the image and
M is the width of the image.
for i=0 to N-1
for j=0 to M-1
gr(xi,yj) = 0.299*r(xi,yj)+0.587*g(xi,yj)+0.114*b(xi,yj)
Here gr(xi,yj) is the grayscale image pixel, r(xi,yj) is the red channel, g(xi,yj)
is the green channel and b(xi,yj) is the blue channel

13. Fig. 2. Scanned Image Fig. 3. Grayscale Image

15.  What is Optical Character
Recognition?
◦ Conversion of Scanned Image
document to Machine Encoded
Text.
◦ Useful in keeping backup of
important documents as text
format.
 Brief History:
◦ 1929-1975: OCR without
Electronic computers
◦ 1985-2000: Development in
OCR for computers
◦ 2000-2013: Developments of
industrial standard OCR
Fig. 4. OCR implementation

16.  Tesseract is currently the best Open Source OCR
Engine.
 Developed at HP between 1984 and 1994.
 Released Tesseract for open source in 2005 and
since then Google has taken over the Project.
Project site:
 Google recently launched Tesseract v3.0
 Used with Java Applications using a JNA wrapper
Tess4J.
Project site: code.google.com/p/tesseractocr

17.  Get outlines by connected
component analysis.
 Organize outlines to
Blobs
 Organize Blobs to Text
Lines
 Characters are chopped
and features are
extracted
Fig. 5. Architecture

18.  Features are extracted using polygonal
approximation.
 Matched with prototype to find matching patterns.
 The adaptive classifier scans the image twice to get
better result the second time.
Fig. 6. Prototype Matching

20.  Why Post Processing?
◦ Corrects errors in the previous step
◦ Gives error free text for Braille Conversion
◦ Spell checking systems provide the best results for post
processing step.
 JOrtho API
◦ JOrtho is an open source Java spell checking API that
gives suggestions for commonly misspelled words in the
text.
◦ The key algorithms include phonetic matching
algorithms such as Soundex
◦ Project site: jortho.sourceforge.net

21.  Soundex Code:
◦ The Soundex Code of a word returns a
alphabet followed by 3 numbers using the
algorithm bellow
 Algorithm:
◦ Retain the first letter of the name and drop
all other occurrences of a, e, i, o, u, y, h, w.
◦ Replace consonants with digits as follows
(after the first letter):
b, f, p, v = 1
c, g, j, k, q, s, x, z = 2
d, t = 3
l = 4
m, n = 5
r = 6
◦ Two adjacent letters with the same number
are coded as a single number. Two letters
with the same number separated by 'h' or
'w' are coded as a single number
Example: “Metacalt”and
“Metacalf” return the same
string M324 as they are
phonetically same
Fig. 7. Spell Cheking

23.  History of Braille:
◦ Invented by Louis Braille in the 19th century
◦ Accepted throughout the world as aform of
written communication for blind individuals
◦ There have been some modifications to the
Braille system such as inclusion of
concatenated words.
 Use of Braille:
◦ Braille is the primary reading and writing
system used by the visually impaired.
◦ Helps in increasing literacy among the
visually impaired.
◦ In modern world Braille technologies are
supported by various electronic devices.
 Braille Cell:
◦ Braille cells are 6-dot cells having
some dots raised or lowered.
◦ 64 possible combinations.
◦ Used in Braille Refreshable Display
Fig. 9. six-dot Braille cell
Fig. 8 Braille Refreshable Display

24.  Braille Details:
◦ Grade 1 and Grade 2 are the most
commonly used.
◦ Grade 1 Braille includes single
letters, numbers while grade 2 Braille
includes concatenated words such as
for,with,you, etc..
◦ Numbers (0,1 to 9) are denoted by
(j,a to i) preceded by the number
denoting cell
◦ Compounds letters (ex: and, with,
wh, the,th…) have separate Braille
representations.
◦ Uppercase alphabets have a
preceding Braille cell denoting
capital letter. Fig. 10. Braille representations

25.  Braille ASCII:
◦ Subset of ASCII character set.
◦ Contains all 64 Braille representations (6-dot cell).
◦ Maps one-to-one ASCII input to Braille code.
◦ Supported by all Braille embossers.
◦ It uses ASCII codes to send information to Braille displays.
 Braille Patterns:
◦ Braille Patterns are Unicode patterns that represent Braille characters.
◦ Consists of 256 combinations of the 8-dot Braille cell. We require
only 64.
◦ Braille embossers and Braille Displays are recently upgraded to
support Unicode Braille.
◦ The Unicode Braille set ranges from U+2800 to U+28FF though we
need only U+2800 to U+283F
◦ In our application, we have focused on Unicode Braille representation.
Braille Code Example:
String: “6 dot Braille Cells for 64 combinations”
Braille:

26.  The flowchart bellow gives
the entire algorithm of
translation.
Fig. 11. Flow Chart for Translation

28.  Extracting Text and correcting errors.
Fig. 12. Extracting Text and Correcting Errors

29.  Translation to Braille
Fig. 13. Converting Text to Braille

31.  We have showed the process of integrating
Tesseract OCR Engine with Braille Translation.
 Our Future plans are to make it multilingual such
that it can support Bharti Braille too which has
Bengali, Hindi, Gujarati and all other Indian
languages.
 We will also provide better support for Grade 2
Braille as Grade 2 Braille is common now-days.
 Project Site: code.google.com/p/brailleocr

32.  [1] Tesseract Project Site: code.google.com/p/tesseractocr
 [2] Chirag Ptel, AtulPatel, Dharmendra Patel, Optical Character
Recognition using Tool Tesseract: A Case Study, IJCA, October 2012
 [3] Pijush Chakraborty and Arnab Mallik, An Open Source Tesseract based
Tool for Extracting Text from Images with Application in Braille
Translation for the Visually Impaired, IJCA, April 2013
 [4] R.Smith, An Overview of the Tesseract OCR Engine, Proc. Ninth Int.
Conference on Document Analysis and Recognition , IEEE Computer
Society (2007)
 [5] Ray Smith, Tesseract OCR Engine, OSCON 2007
 [6] Tess4J Project Site: http://tess4j.sourceforge.net/
 [7] JOrtho Project Site: http://jortho.sourceforge.net/
 [8] Soundex Reference: http://en.wikipedia.org/wiki/Soundex
 [9] The Rules of Unified English Braille, International Council on English
Braille(ICEB), June 2001
 [10] Braille ASCII: http://en.wikipedia.org/wiki/Braille_ASCII
 [11] BrailleOCR Project Site: code.google.com/p/brailleocr

33. Questions?