The document outlines a project on text extraction from images, detailing the process from pre-processing to recognition. Methods such as noise removal, segmentation, and various recognition techniques like PCA and neural networks are discussed. Applications of the technology include banking, libraries, government processing, and recognition systems like car number plates.

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. 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. Goal Of Project
GENERAL APTITUDE
Computer Science
Electronics &
Communication
Engineering

4. How Will We Archive That Goal ??
Pre
processing
Segmentation
Recognition

5. Pre-Processing

6. Pre-Processing
Gray Scale
Noise
Removal
Thresholding

7. Gray Scale

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. Thresholding
Edge Detection
Dilate Image
Detect Text Area Using Histrogram
Personal Thresholding to Text Area

10. Edge Detection using Canny

11. Dilate

12. Text Area Using Histrogram

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. Segmentation

15. Segmentation
Line
Segmentation
Word
Segmentation
Character
Segmentation

16. TEXT SEGMENTATION
From above Image, Image are segment in
to Different Lines, Below an example of
Only For one Line.

17. Segmentation
Find all the word than convert text
area in one image
Character are separate from the
word

18. Recognition

19. Recognization
Feature
Extraction
Classifier
Text Document

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. 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. 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. Thank You