The Aim Of This Project Is To Develop A Web Application That Can Extract Text From An Image And Translate It To A Desired Language. The Application Is Built Using The Tesseract OCR Engine And The Flask Web Framework In Python. The Tesseract OCR Engine Is Used To Extract Text From The Image And The Flask Web Framework Is Used To Build The Web Application. The User Can Upload An Image Containing Text In Any Language. The Image Is Processed Using Tesseract OCR Engine To Extract The Text. The Extracted Text Is Then Translated To The Desired Language Using A Translation API. The Translated Text Is Displayed On The Web Page. The Application Also Provides An Option For The User To Select The Language They Want To Translate The Text To. The User Can Select The Desired Language From A Dropdown Menu On The Web Page. The Application Supports A Wide Range Of Languages For Translation. The Application Is Designed To Be User-friendly And Easy To Use. The User Interface Is Simple And Intuitive. The User Can Upload The Image And Select The Language They Want To Translate To With Just A Few Clicks. The Application Is Also Scalable And Can Handle Large Volumes Of Image And Text Data.
The Development Of Information Technology Has Been Increasingly Changing The Means Of Information Exchange Leading To The Need Of Digitizing Print Documents. In The Present Era, There Is A Lot Of Fraud That Often Occurs. For Example, Is Account Fraud, To Avoid Account Fraud There Was Verification Using ID Card Extraction Using OCR And NLP. Optical Character Recognition (OCR) Is A Technology That Used To Generate Text From Images. With OCR We Can Extract Aadhar Card Into Text Using Pytesseract. To Improve The Accuracy We Made Text Corrections Using Natural Language Processing (NLP) Basic Tools To Fixing The Text. With 5 Aadhar Card Image, We Compared The Performance With Three Different OCR Libraries. The Result Of Our Experiment Shows That Pytesseract Had The Best Performance.The Resultant Edge Image Contains The Broken Characters. To Fill These Gaps, We Apply The Dilation Operator That Increases The Thickness Of The Characters. Dilation Fills The Broken Characters, However, Also Add Extra Thickness That Is Then Removed Through Applying The Morphological Thinning. Finally, Dilation And Thinning Are Applied In Combination To Optical Character Recognition (OCR) To Segment And Recognize The Characters Including The Name, ID, DOB, Gender And Photo Of Person.
Exponential Growth Of Fake ID Cards Generation Leads To Increased Tendency Of Forgery With Severe Security And Privacy Threats. University ID Cards Are Used To Authenticate Actual Employees And Students Of The University. Manual Examination Of ID Cards Is A Laborious Activity, Therefore, In This Paper, We Propose An Effective Automated Method For Employee/student Authentication Based On Analyzing The Cards. Additionally, Our Method Also Identifies The Department Of Concerned Employee/student. For This Purpose, We Employ Different Image Enhancement And Morphological Operators To Improve The Appearance Of Input Image Better Suitable For Recognition. More Specifically, We Employ Median Filtering To Remove Noise From The Given Input Image.
Rigorous Research Has Been Done On Ancient Indian Script Character Recognition. Many Research Articles Are Published In Last Few Decades. Number Of OCR Techniques Is Available In Market, But OCR Techniques Are Not Useful For Ancient Script Recognition. But More Research Work Is Required To Recognize Ancient Marathi Scripts. This Paper Presents Different Techniques Which Are Published By Different Researchers To Recognize Ancient Scripts. Also Challenges In Recognition Of Ancient Marathi Scripts Are Discussed In This Paper.
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