This document summarizes a student project to build a naïve optical character recognition (OCR) system. The students limited their system to one font from one book, lowercase characters only, and pictures taken with one phone. They collected data from 44 pictures containing 242 words. Their workflow involved preprocessing images then extracting 8x16 pixel feature vectors for each character. They used a support vector machine classifier with a radial basis function kernel, achieving 98.86% accuracy on 10-fold cross-validation of their data, compared to 91.47% for commercial Tesseract OCR. Key lessons were that character frequencies vary significantly from English, not all fonts separate easily into characters, and installing libraries was more difficult than expected.

1. BUILDING A NAÏVE OCR
SYSTEM
Kaur Alasoo and Jaana Metsamaa
Data Mining (2009 fall)
28 January 2010 http://kaur.pri.ee/projects/wordocr/

2. VISION
Take a
snapshot of a
page.

3. VISION
Extract one
word from
the picture.

4. VISION
Look up the word
on Google,
dictionary, etc...

5. REALITY
• To increase the likelihood of success, we limited ourselves to:
• One font from one book.
• Only lowercase characters.
• Pictures taken with one phone.

6. GETTING THE DATA
44 pictures

7. GETTING THE DATA
44 pictures 242 words

8. 0. Take a picture
1. Convert to grayscale
2. Apply thresholding
WORKFLOW 3. Cut horizontally
4. Cut vertically
5. Resize
8x16 px

9. FROM PICTURE TO FEATURE
VECTOR

10. FROM PICTURE TO FEATURE
VECTOR

11. FROM PICTURE TO FEATURE
VECTOR
255

12. FROM PICTURE TO FEATURE
VECTOR
255 255

13. FROM PICTURE TO FEATURE
VECTOR
255 255 255

14. FROM PICTURE TO FEATURE
VECTOR
255 255 255 0

15. FROM PICTURE TO FEATURE
VECTOR
255 255 255 0 0

16. FROM PICTURE TO FEATURE
VECTOR
255 255 255 0 0 155

17. FROM PICTURE TO FEATURE
VECTOR
255 255 255 0 0 155 255

18. FROM PICTURE TO FEATURE
VECTOR
255 255 255 0 0 155 255 255

19. FROM PICTURE TO FEATURE
VECTOR
255 255 255 0 0 155 255 255

20. TRAINING THE CLASSIFIER
We used 1343 characters to train a SVM with RBF
kernel.

21. RESULTS
• 10-fold cross-validation accuracy with our method:
98.86%
• Character classification accuracy using Tesseract OCR:
91.47%

22. WHAT WE LEARNED?

23. SOME LETTERS ARE
EXTREMELY RARE
15
English Our data
11
8
4
0
e t a o i n s h r d l c u m w f g y p b v k j x q z

24. NOT ALL FONTS CAN BE EASILY
SEPARATED INTO CHARACTERS

25. WHAT WE LEARNED?
• Installing libraries is the most difficult thing.
• Do not overly restrict yourself.
• Donot build your on OCR system unless its absolutely
necessary.
• The letters are the easy part.
http://kaur.pri.ee/projects/wordocr/