Text extraction From Digital image

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Text Extraction is a process by which we convert Printed document/Scanned Page or Image in which text are available to ASCII Character that a Computer can Recognize.

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Text extraction From Digital image

  1. 1. Prepared By: Amit Bhoraniya (7022) Kaushik Godhani(7009) Mayur Halai(7016) Vikram Ghunsar(7039) Text Extraction From Image Guided By: Mr. Udesang Jaliya Mr. Kirti Sharma
  2. 2. What is Text Extraction ?? Text Extraction is a process by which we convert Printed document/Scanned Page or Image in which text are available to ASCII Character that a Computer can Recognize.
  3. 3. Goal Of Project GENERAL APTITUDE Computer Science Electronics & Communication Engineering
  4. 4. How Will We Archive That Goal ?? Pre processing Segmentation Recognition
  5. 5. Pre-Processing
  6. 6. Pre-Processing Gray Scale Noise Removal Thresholding
  7. 7. Gray Scale
  8. 8. Noise Removal Noise Removal is used to Enhance the Image For Enhancing We have used Median Filter  FilteredImage = Median Filter(Origional Image, FilterSize)  We have used FilterSize [5,5]
  9. 9. Thresholding Edge Detection Dilate Image Detect Text Area Using Histrogram Personal Thresholding to Text Area
  10. 10. Edge Detection using Canny
  11. 11. Dilate
  12. 12. Text Area Using Histrogram
  13. 13. Algorithm • Row Histrogram • Separate Region by (no. of Pixel > 60 ) • For Each Row – Separate Region by (no. of Pixel > Height of (Row/4))
  14. 14. Segmentation
  15. 15. Segmentation Line Segmentation Word Segmentation Character Segmentation
  16. 16. TEXT SEGMENTATION From above Image, Image are segment in to Different Lines, Below an example of Only For one Line.
  17. 17. Segmentation Find all the word than convert text area in one image Character are separate from the word
  18. 18. Recognition
  19. 19. Recognization Feature Extraction Classifier Text Document
  20. 20. Recognization • Feature Extraction • Binary Code Method • Chain Code Method • PCA (Principle Component Analysis) • LDA (Linear Discriminative Image) • Classifier • Artificial Neural Network • Support Vector Machine
  21. 21. Applications • Banking (To read Credit Card) • Libraries (To convert Scanned Page to Image) • Govt. Sector (Form Processing) • Used in Car Number Plate Recognition System • Undesirable Text removal from images.
  22. 22. References 1. OCR for Devnagari Script by Mahesh Goyani 2. Edge Based Text Extraction From Complex Images by Xiaoqing Liu and Jagath Samarbandhu 3. Automatic Text Detection using Morphological Operations and Inpainting by Khyati Vaghela 4. Font and Background Color Independent Text Binarization by T.Kasar , J.Kumar , A.G. Ramkrishnan
  23. 23. Thank You

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