Optical Character Recognition (OCR) is a technology that converts non-digital text into editable formats. It works by recognizing printed or written characters using computer vision techniques. The document describes the architecture and objectives of an OCR system, including converting documents to text, speeding up processing, and embedding in applications. It outlines common OCR methods such as grayscaling, binarization, noise removal, sharpening, segmentation, feature extraction, and recognition to identify characters. Diagrams show the system architecture and workflow. Screenshots demonstrate the developed OCR system in use. The conclusion discusses automatic data entry and future areas like recognizing handwriting.

1. Optical Character
Recognition (OCR)
Supervisor : Er. Bishnu Hari Poudel
Team Members:
Binay Thapa Magar (070/611)
Neeraj Neupane (070/622)
Prem Kumari Pun (070/627)
Sumit Gautam (070/642)

2. Overview
● a technology that enables
converting non-digital files
into editable text files
●recognition of printed or
written text characters by a
computer

3. Architecture of OCR
GRAYSCALE &

4. Objectives
● Converting paper document into editable text
format
● Speeding up character recognition in
document processing
● Embedding to different application systems
● Creating an OCR System for uni-font and uni-
text size recognition

5. Methodologies
Used in OCR
1. Grayscaling
2. Binarization
3. Noise Removing
4. Image Sharpening
5. Line - Word - Character
Segmentation
6. Feature Extraction
7. Recognition

6. Method 1 : Grayscaling
● Normal image is converted to Grayscale image
● This image has equal intensity of Red, Green and Blue
colors
● Algorithm used : Luminosity Method
Normal Image Grayscale Image

7. Method 2 : Binarization
● Converting the image into black and white representation.
i.e., intensity information will be reduced to two values
respectively 0 and 1.
● Algorithm used : Otsu’s Algorithm
Grayscale Image
Binarized Image

8. Method 3 : Noise Removing
● Noise: Random fluctuations in
brightness, color and intensity that
usually make it more difficult to
process image properly
● Salt-Pepper Noise (Common
Noise in Images)
● This will have dark pixels in bright
regions and bright pixels in dark
regions
Example of Salt-Pepper Noise

9. Method 4 : Image Sharpening
● Algorithm used : Erosion and Dilation Algorithm
● Erosion reduces image by getting rid of small
extrusions
● Dilation expands the region as it tends to fill out
small intrusions
Dilation Erosion

10. Method 5 : Line - word - character segmentation
● Separate different parts of the image i.e. isolate one word from another
and then separate letters of the word.
● Algorithm used : Histogram and Mathematical Computation
Segmentation

11. Method 6 : Feature Extraction
● Finding a set of features that define the shape of the
underlying character as precisely and Uniquely as
possible.
● Algorithm used : Zoning Method

12. Method 7 : Classification
● Use extracted features to identify character according
to preset rules.
● Determining region of feature space in which the
unknown pattern falls.
● Algorithm used : K- Nearest Neighbor Classification
Method

13. Use Case Diagram of OCR System

14. Sequence Diagram of OCR System

15. Screenshots of OCR System

16. Screenshots of OCR System

17. Screenshots of OCR System

18. Conclusion and
Future Scopes
● Automatic data entry is most
attractive and lucrative
technology
● Saving time and space with
electronic data entry procedure
● OCR can be used for Research
and Development Purposes
● Handwritten documents
recognized in future

19. Some References
1. https://github.com/tesseract-ocr/tesseract
2. https://www.youtube.com/watch?v=uU1LHp-wFDQ
3. https://www.scribd.com/doc/87514317/Project-Report-OCR
4. https://www.iiti.ac.in/people/~tanimad/JavaTheCompleteReference.pdf
5. https://www.robotix.in/tutorial/imageprocessing/noise.html
6. http://ksuweb.kennesaw.edu/~plaval/research/mean/noise/ocr.htm
7. http://www.felixniklas.com/imageprocessing/binarization
8. http://www.felixniklas.com/imageprocessing/segmentation
9. https://www.tutorialspoint.com/dip/grayscale_to_rgb_conversion.htm
10. Digital Image Processing Using MATLAB by R.C. Gonzalez

20. THANK YOU