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This project provides, Support of Extraction, Verification and Masking of Aadhaar UIDs from photos and scanned documents. The solution to the problem involves use of PyTesseract Optical Character Recognition engine and OpenCV for image processing.

In today's world scams are being done by taking personal information. Aadhaar card is one of thedocument which can be used to get information of a person because it has its address, mobilenumber, personal information and also bank accounts attached to it so to prevent that informationaadhaar card number has to be hidden not completely but partially like account number so thatpersonal and financial information can be preserved so that frauds can be reduced to a certain extent.So we aimed to build aCNN model to mask the partialUID (Unique Identification) of the aadhaar irrespective of the format and orientation of aadhaar provided. This model achieved an accuracy of 94.6 % for both training as well as validation.

Note: For testing purposes we have used a publically available Aadhaar Sample. Which is available on (uidai.gov.in), We have not harmed anyone's privacy.

App window will show the all available UID in the Aadhar

In the last step it will save the Masked Copy in the project folder itself.

• It can do batch operations and save the final output in.tiff fileformate.
• Provide excellent PDF merging support.
• Able to recognize UID from any type of file format such asjpeg, png, pdf etc.
• The language is different in Aadhaar in differentregions and this is a very critical problem, but our model can alsorecognize different languages easily.

In our solution pipeline we use some algorithms such as:

1.Verhoeff Algorithm: Aadhaar UID is a 12-digit number in which the last digit is a checksum digitcalculated using this algorithm. It utilizes some tables (multiplication, inverse and permutation) forcalculating the checksum bit. For validating, the same tables are used.

2.ESRGANs: Enhanced Super-Resolution Generative Adversarial Networks are capable ofgenerating realistic textures during single image super-resolution. It achieves better visual qualitywith more realistic and natural textures than the original picture.

Note: Because of some dependencies issues with Tensorflow 2.0, We have used Python 3.7.9, This project is perfectly working on Windows machines but causing some issues with Linux based systems.

Step-1: Clone this repo and Install Python 3.7.9 in your machine.

Step-2: Installrequirements.txt by entering below command in Windows Command Prompt

pip install -r requirements.txt

Step-3: Extractpoppler-0.68.0_x86 inC:\Program Files and provide the path of bin that isC:\Program Files\poppler-0.68.0\bin in the main code.

Step-4: Installtesseract-ocr-w64-setup-v5.0.0-alpha.20201127 and provide its path aspytesseract.pytesseract.tesseract_cmd =r'C:\Program Files\Tesseract-OCR\tesseract' in code snippet.

Step-5: Run the code Masking.py

All Done!