Presentation on OCR of noisy images using MATLAB
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7/28/2019 Presentation on OCR of noisy images using MATLAB
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Objective
Develop a prototype of OCR system Application of Template Matching for Recognition
Present scope Machine reading characters Source of characters:
Typewritten Handwritten/ Photographs
Efficiently reads : upper/ lower case alphabets numerals 0-9 multiple lines and
noisy images as well
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What is OCR ?
OCR : Optical Character Recognition Digitizing printed texts
Purpose ?
Support full editing & searching Compact storage capability
Editing enabled E-storage saves paper; environment friendly Enables online display of information Speed up all related process. and many more
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Ways to implement OCR: Transformation and series expansion Template Matching Structural Analysis Artificial Neural Network
etc
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What are its applications ?
Mass surveillance methods Face/ Iris etc. recognition systems Banking: process cheque without human intervention At offices: to process paper work Reading machine for the blind etc.
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Input image from camera/scanner/snapshots
Convert image into binary
Remove Noise
Segmentation
Character Identification
Save to file in a text format
General Algorithm
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Flow of Control for character recognition
Input image :typewritten/photograph
Extract 1 line at a time
From the detected string select thecharacter image
Rescale the image to the size of thetemplate
Match: Image & Template
Store the highest match found; in
case of no match repeat theprevious step
The best match is stored as therecognized character
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Database
Store the characters corresponding tofigures which are considered ideal. Times New Roman/ Calibri Font Size: 42*24
62 elements, in totality
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Testing the system
Input images from the real world were made as input to our system andsatisfactory results were obtained .
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Conclusion drawn from results:
The ambiguous characters, e.g. B, 8 etc have lower recognition
rate.
Possible reasons:
a) Characters similarity with other characters (e.g. B and 8, Sand 5, 1 and l) etc.
b) Image quality, font of the characters
c) Techniques limitation
d) Accuracy rate of 77.78% for noisy images, calculated on the
basis of test results.(18-4)/18*100 = 77.78 %
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Text with a font size < 14 will results in more errors.
Most documents formatting are lost during text scanning, so their
recognition depends on how well the document is scanned.
The output will always require spellchecking and proofreading aswell as reformatting to get the desired final layout.
These models are although performing good and are widely appliedbut they are no where near to the performance of the human brain!
Rather they can never be there!
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Future Scope
Use more adaptive technique (ANN) to implement OCR.
Also we would like to improve on the results weve obtained for
noisy images.
Our ultimate goal will be to accomplish 99% accuracy!
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Thank You
*****
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