The document summarizes a seminar presentation on improvements to optical character recognition (OCR) algorithms. It discusses how OCR works to convert images of text into machine-encoded text, along with its applications. The presentation compares OCR to similar technologies like intelligent character recognition and describes the typical components of OCR systems. It notes that while OCR accuracy has improved, full accuracy is still not achieved for handwritten text and other scripts. The presentation concludes that accuracy can be increased through human review and using data dictionaries during automatic correction.

1. A SEMINAR
PRESENTATION
ON
IMPROVEMENTS IN OCR ALGORITHM
Seminar Guide :
Mr. Deepak Agarwal
Seminar Coordinator :
Mr. Abhishek Gupta
Presented By:
Rahul Taneja
PCE/CE/13/146
IV Year CS B
CSE Department, PCE

2. CONTENTS
1. Introduction
2. Applications
3. Comparison of Few similar technologies/ Algorithms/ Approaches
4. Working Architecture/Component/Methods
5. Limitations
6. Future Scope
7. Conclusion
8. References
9. Queries
CSE Department, PCE

3. 1. Introduction
• Optical character recognition is the mechanical or electronic conversion
of images of typed, handwritten or printed text into machine-encoded text.
CSE Department, PCE

4. • It is a common method of digitising printed texts so that they can be
electronically edited, searched and stored more compactly.
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5. 2.Applications
• Data entry e.g. check, passport, invoice, bank statement and receipt
• Automatic number plate recognition
• Speech Synthesis
• Textual versions of printed documents, e.g. book scanning
• Defeating CAPTCHA anti-bot systems, though these are specifically
designed to prevent OCR
• Assistive technology for blind and visually impaired users
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6. 3.Comparison of Few similar technologies/
Algorithms/ Approaches
• Intelligent Character Recognition (ICR) is an extended technology of OCR
(optical character recognition).
• ICR is about hand written characters that are separated and written
as single characters.
• OCR is designed to work on printed characters while ICR is focusing
on hand printed characters.
• To enhance the ICR recognition accuracy it is common in all technologies
to use meta data, for example:
• regular expression
• name/city and other dictionaries
• database look ups
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7. 4.Working Architecture/Component/Methods
• Broadly speaking, there are two different ways to solve this problem,
1. by recognizing characters in their entirety (pattern recognition) or
2. by detecting the individual lines and strokes characters are made from
(feature detection) and identifying them that way
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8. 5.Limitations
• Recognition of typewritten text is still not 100% accurate even where
clear imaging is available. Character-by-character OCR accuracy for
commercial OCR software varies from 81% to 99%
• Recognition of cursive handwriting, and printed text in other scripts
are still the subject of active research.
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9. 6.Future Scope
• Total accuracy can be achieved by:
1. Human review (Manual Correction)
In this, Humans intervention is required as the word may not be available in data
dictionary
2. Data Dictionary Authentication (Automatic Correction)
In this, the Algorithm searches for the unrecognized pattern or feature in a data
dictionary which then provides with the probable correct pattern or feature.
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10. 7.CONCLUSION
• Thus we can now conclude that :
1. OCR algorithm works well for printed text and fairly for hand written text
2. It needs research for cursive handwritten text
3. Accuracy is not 100% even if the input provided is in its best form
4. Accuracy can be improved by implementing
1. Human review
2. Data dictionary authentication
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11. 8.REFERENCES
• Research papers :-
1. OCR accuracy improvement by Jyoti Goyal, CLDU, Sirsa.
2. Automatic Number plate recognition by Mohd. Arif, University of
Karachi.
• www.Wikipedia.com
• www.explainthatstuff.com
CSE Department, PCE

12. 9.Queries