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BrailleOCR: An Open Source Document to Braille Converter Application

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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.

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BrailleOCR: An Open Source Document to Braille Converter Application

  1. 1. Calcutta Institute of Engineering and Management CS681 Seminar
  2. 2.  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
  3. 3.  Conversion of an Image Document to Braille consists of the following steps: Fig. 1. Steps to be Followed
  4. 4.  Conversion of an Image Document to Braille consists of the following steps: Fig. 1. Steps to be Followed
  5. 5.  Conversion of an Image Document to Braille consists of the following steps: Fig. 1. Steps to be Followed
  6. 6.  Conversion of an Image Document to Braille consists of the following steps: Fig. 1. Steps to be Followed
  7. 7.  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
  8. 8.  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
  9. 9. Fig. 2. Scanned Image Fig. 3. Grayscale Image
  10. 10.  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
  11. 11.  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
  12. 12.  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
  13. 13.  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
  14. 14.  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
  15. 15.  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
  16. 16.  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
  17. 17.  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
  18. 18.  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:
  19. 19.  The flowchart bellow gives the entire algorithm of translation. Fig. 11. Flow Chart for Translation
  20. 20.  Extracting Text and correcting errors. Fig. 12. Extracting Text and Correcting Errors
  21. 21.  Translation to Braille Fig. 13. Converting Text to Braille
  22. 22.  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
  23. 23.  [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
  24. 24. Questions?
  • dragon515

    Jan. 13, 2015
  • pijush15

    Apr. 29, 2014

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.

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