Presentation on OCR of noisy images using MATLAB

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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Documents

Presentation on OCR of noisy images using MATLAB