An Introduction demo on

Mobile Camera Based Text Detection & Translation

Under The Guidance Of: Prof. Shweta Patil.

Presented By: Akash Y Shindhe

Manjunath N NayakSachin B Biradar

Vallabh G Potadar

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Contents

Introduction History Existing System Proposed System System Flow Requirement Block Diagram Test & Results Applications Advantages & LimitationConclusionBibliography

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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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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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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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Working Diagram

Fig. a: Working diagram

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System flow

Algorithms:

Edge detection

Image feature filtering

Image binarization

Optical character recognition

Text correction

Text translation

Display of translation

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RequirementMobile 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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Block Diagram

Captured Image

Text Feature Filtering

Google APIs

File Library

Match Image

Retrieve Text

Translate Text

Display Output Text

Fig. b: Block diagram

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Example

Fig. c: Example

c.1 c.2

c.3 c.4

c.5

Fig c.1Fig c.2Fig c.3Fig c.4Fig c.5

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Test & ResultsImage 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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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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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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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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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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Bibliography1. 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