1. An Introduction demo on
Mobile Camera Based Text Detection & Translation
Under The Guidance Of: Prof. Shweta Patil.
Presented By:
Akash Y Shindhe
Manjunath N Nayak
Sachin B Biradar
Vallabh G Potadar

2. Contents
 Introduction
 History
 Existing System
 Proposed System
 System Flow
 Requirement
 Block Diagram
 Test & Results
 Applications
 Advantages & Limitation
 Conclusion
 Bibliography
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3. Introduction…
Our project ‘Mobile camera based text detection and
translation’ retrieves text from an images and converts it into
text format, then it is translated to specified language.
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4. Existing System
 In 1929, first OCR device was invented but it was mechanical device
 In about 1965, earliest form of OCR was implemented in one of the
first generation computers for Airline Ticket stock.
 Revolutionary in 1971, it was implemented in postal services OCR
systems where reading and printing of routing bar code was done on
the postal code.
 In 1974, the modifications was done which would allow blind
people to have a computer read text to them out loud.
 In late 90’s, Webcam was used for OCR process.
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5. Working…
 Capture image
 Detect edges
 Detect corners
 Match with stored image file
 Retrieve text from image
 Translate using Google API
 Show Result
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6. Working Diagram
Fig. a: Working diagram
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7. System flow
 Algorithms:
 Edge detection
 Image feature filtering
 Image binarization
 Optical character recognition
 Text correction
 Text translation
 Display of translation
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8. Requirement
Mobile Hardware Requirements:
• ARM 11 processor or higher
• Memory 1 GB
• 256 MB RAM
• Mobile camera 3.2 mega pixel and above
Software Requirements:
• Operating System – Android Mob OS 2.2+
• Windows 7 OS
• Mat lab OCR,ADT bundles
Communication Requirements:
• Internet Connection is required
• Android Mobile OS inbuilt web browser
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9. Block Diagram
Captured
Image
Text Feature
Filtering
Match
Image
File Library
Retrieve
Text
Google
APIs
Translate
Text
Display
Output Text
Fig. b: Block diagram
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10. Example
c.1
c.2
c.5
Fig c.1
Fig c.2
Fig c.3
Fig c.4
Fig c.5
c.3
c.4
Fig. c: Example
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11. Test & Results
Image quality :
As image quality degraded recognition rate will decrease
Recognition rate of character ‘A’ , ‘B’ , ‘L’ will be higher than recognition
rate of character ‘y’ , ‘u’ , ‘c’.
Fig. d: Test & result
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12. Applications
Tourist understanding native language.
 Instant recognition of texts, street and e-mail
addresses, links, and telephone numbers.
 Unknown language guideline.
 Easy to recognize road signs scripts.
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13. Advantages
 Android Mobile OS based platform.
 No tiresome manual data entry.
 Versatility and ease of use.
 No database is needed
 For data entry
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14. Limitations
 Image taken by Mobile camera should be of good quality.
 Mobile should be of high specifications
 For translation of extracted text , Internet connection is
required.
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15. Conclusion
This project which we want to implement is an Android
Mobile OS based application which is web based real time mobile
application for real-time text extraction, recognition and
translation.
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16. Bibliography
1. Michael Hsueh “Interactive Text Recognition and Translation on a Mobile Device “
[Technical Report No. UCB/EECS-2011-57 ]
2. Yassin M.Y.Hasan and Lina J.Karam “Morphological Text Extraction from Images” IEEE
Transaction on Image Processing Vol.9 No.11, Nov 2000
3. Nobuyuki Otsu, A threshold selection method from gray-level histograms. IEEE
Trans.Sys.,Man., Cyber 9(1):62-66
4. Celine Mancas-Thillou, Bernard Gosselin, Color text extraction with selective metric
based clustering. Computer Vision and Image Understanding 2007
5. B. Epshtein, Detecting Text in Natural Scenes with Stroke Width Transform. Image
Rochester NY, pp. 1-8.
6. Derek Ma , Qiuhau Lin, Tong Zhang “Mobile Camera Based Text Detection and
Translation” – research paper
7. WWW.wikipedia.org/optical_character_recognization
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