A Seminar OnMobile Camera Based Text Detection & Translation<br />Under The Guidance Of:   Prof. Gaikwad K.P.<br />Present...
Contents<br /><ul><li> Introduction
 History
Present
 Working
 System Flow
Requirement
Block Diagram
 Test & Results
UML diagrams
Applications
Advantages &Limitation
 Conclusion
 Bibliography </li></ul>2<br />
Introduction…<br />		Our project ‘Mobile camera based text detection and translation’ retrieves text from an images and co...
History<br /><ul><li> 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 computersfor Airline Ticket stock.
 Revolutionary in 1971, it was implemented in postal services OCR systems where reading and printing of routing bar code w...
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.</li></ul>4<br />
Present<br /><ul><li>Webcam integrated with computers are being used for capturing image and easily the text can be extrac...
 The image can be analyzed and translated also online.
 Only some software companies manufactures the OCR system in mobile , having high specifications.
ABBYY Mobile OCR, is the leading manufacture of mobile OCR.</li></ul>5<br />
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Mob ocr

  1. 1. A Seminar OnMobile Camera Based Text Detection & Translation<br />Under The Guidance Of: Prof. Gaikwad K.P.<br />Presented By:Mr. Vivek Kumar<br />
  2. 2. Contents<br /><ul><li> Introduction
  3. 3. History
  4. 4. Present
  5. 5. Working
  6. 6. System Flow
  7. 7. Requirement
  8. 8. Block Diagram
  9. 9. Test & Results
  10. 10. UML diagrams
  11. 11. Applications
  12. 12. Advantages &Limitation
  13. 13. Conclusion
  14. 14. Bibliography </li></ul>2<br />
  15. 15. Introduction…<br /> 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.<br />3<br />
  16. 16. History<br /><ul><li> In 1929, first OCR device was invented but it was mechanical device
  17. 17. In about 1965, earliest form of OCR was implemented in one of the first generation computersfor Airline Ticket stock.
  18. 18. 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.
  19. 19. In 1974, the modifications was done which would allow blind people to have a computer read text to them out loud.
  20. 20. In late 90’s, Webcam was used for OCR process.</li></ul>4<br />
  21. 21. Present<br /><ul><li>Webcam integrated with computers are being used for capturing image and easily the text can be extracted by it and than translated.
  22. 22. The image can be analyzed and translated also online.
  23. 23. Only some software companies manufactures the OCR system in mobile , having high specifications.
  24. 24. ABBYY Mobile OCR, is the leading manufacture of mobile OCR.</li></ul>5<br />
  25. 25. Working…<br /><ul><li> Capture image
  26. 26. Detect edges
  27. 27. Detect corners
  28. 28. Match with stored image file
  29. 29. Retrieve text from image
  30. 30. Translate using Google API
  31. 31. Show Result</li></ul>6<br />
  32. 32. Working Diagram<br />Fig. a: Working diagram<br />7<br />
  33. 33. System flow<br /><ul><li>Algorithms:
  34. 34. Edge detection
  35. 35. Image feature filtering
  36. 36. Image binarization
  37. 37. Optical character recognition
  38. 38. Text correction
  39. 39. Text translation
  40. 40. Display of translation</li></ul>8<br />
  41. 41. Edge Detection<br />1. Convert Input image into Gray – scale image :<br />Y = 0.299R + 0.587G + 0.114B<br />2. Apply Blurring on image Y .<br />3. Find threshold value of Y2 =<br />9<br />
  42. 42. Text feature filtering:<br />10<br />After Detecting Text Area, the Extraction of the character from the image is perform<br />For Extraction & detection of the character the Edge detection, corner detection used.<br />
  43. 43. Requirement<br />Mobile Hardware Requirements:<br /><ul><li>ARM 11 processor or higher
  44. 44. Memory 1 GB
  45. 45. 256 MB RAM
  46. 46. Mobile camera 5 mega pixel</li></ul>Software Requirements:<br /><ul><li> JAVA - J2ME and J2EE
  47. 47. Operating System – Android Mob OS</li></ul>Communication Requirements:<br /><ul><li> Internet Connection is required
  48. 48. Android Mobile OS inbuilt web browser</li></ul>11<br />
  49. 49. Block Diagram<br />Text Feature Filtering<br />Captured Image<br />Google Translator<br />Match Image <br />Retrieve Text<br />Translate Text<br />File Library<br />Display Output Text<br />Fig. b: Block diagram<br />12<br />
  50. 50. Example<br />c.1<br />c.2<br />c.5<br />Fig c.1<br />Fig c.2<br />Fig c.3<br />Fig c.4<br />Fig c.5<br />c.4<br />c.3<br />Fig. c: Example<br />13<br />
  51. 51. Test & Results<br />Font :<br /> Recognition rate does not vary as font changes<br />Font size : <br /> As the size of text varies , Recognition rate will vary i.e. if the text is of larger size then recognition rate will be greater.<br />14<br />
  52. 52. Test & Results<br />Image quality :<br /> As image quality degraded recognition rate will decrease<br />Recognition rate of character ‘A’ , ‘B’ , ‘L’ will be higher than recognition rate of character ‘y’ , ‘u’ , ‘c’.<br />Fig. d: Test & result<br />15<br />
  53. 53.
  54. 54.
  55. 55. Applications<br /><ul><li>Tourist understanding native language.
  56. 56. Instant recognition of texts, street and e-mail addresses, links, and telephone numbers.
  57. 57. Unknown language guideline.
  58. 58. Easy to recognize road signs scripts.</li></ul>18<br />
  59. 59. Advantages<br /><ul><li> Android Mobile OS based platform.
  60. 60. No tiresome manual data entry.
  61. 61. Versatility and ease of use.
  62. 62. No database is needed</li></ul>19<br />
  63. 63. Limitations<br /><ul><li>Image taken by Mobile camera should be of good quality.
  64. 64. Many arithmetical equations cannot be recognized correctly.
  65. 65. Mobile should be of high specifications
  66. 66. For translation of extracted text , Internet connection is required.
  67. 67. Translated text may have Grammatical mistakes</li></ul>20<br />
  68. 68. Conclusion<br /> This project which we have implemented is an Android Mobile OS based application which is web based real time mobile application for real-time text extraction, recognition and translation. <br />21<br />
  69. 69. Bibliography<br />Michael Hsueh “Interactive Text Recognition and Translation on a Mobile Device “ [Technical Report No. UCB/EECS-2011-57 ]<br />YassinM.Y.Hasan and LinaJ.Karam “Morphological Text Extraction from Images” IEEE Transaction on Image Processing Vol.9 No.11, Nov 2000<br />Nobuyuki Otsu, A threshold selection method from gray-level histograms. IEEE Trans.Sys.,Man., Cyber 9(1):62-66<br />Celine Mancas-Thillou, Bernard Gosselin, Color text extraction with selective metric based clustering. Computer Vision and Image Understanding 2007 <br />B. Epshtein, Detecting Text in Natural Scenes with Stroke Width Transform. Image Rochester NY, pp. 1-8.<br />Derek Ma , Qiuhau Lin, Tong Zhang “Mobile Camera Based Text Detection and Translation” – research paper<br />WWW.wikipedia.org/optical_character_recognization<br />22<br />
  70. 70. Any Question..??<br />
  71. 71. Thank yOu..!!<br />
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