Building a Naive OCR System

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

VISION Take a snapshot of a page.

VISION Extract one word from the picture.

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

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

GETTING THE DATA 44 pictures

GETTING THE DATA 44 pictures 242 words

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

FROM PICTURE TO FEATURE VECTOR

FROM PICTURE TO FEATURE VECTOR 255

FROM PICTURE TO FEATURE VECTOR 255 255

FROM PICTURE TO FEATURE VECTOR 255 255 255

FROM PICTURE TO FEATURE VECTOR 255 255 255 0

FROM PICTURE TO FEATURE VECTOR 255 255 255 0 0

FROM PICTURE TO FEATURE VECTOR 255 255 255 0 0 155

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

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

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

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

WHAT WE LEARNED?

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

NOT ALL FONTS CAN BE EASILY SEPARATED INTO CHARACTERS

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/

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Building a Naive OCR System

by Kaur Alasoo on Jan 28, 2010

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