This project scans for any personal information in images that you wouldn't want out in the internet (like phone numbers, credit card numbers, names, addresses, IP addresses, etc) by using Optical Character Recognition, Natural Language Processing, Regular Expressions and object detection systems.

Table of Contents

• Introduction
  
• Requirements
  
• How to use
  
• Contribution

Introduction

Personal Information in images is often overlooked, especially when posting to sites like Instagram where everyone can see the pictures you upload. People seldom check if there is any information in the background of their pictures that might leak personal information on a public site. My project tries to automate this process by checking the image for information that can be harmful if leaked in the public.

Table of Contents

• Introduction
  
• Requirements
  
• How to use
  
• Contribution

Requirements

python 3.10.12
flair 0.12.2
streamlit 1.27.1
easyocr 1.7.1
matplotlib 3.8.0
opencv 4.8.1.78
numpy 1.26.0
regex 2023.8.8

Installation and usage

• Clone the repository
• If you have a supported GPU, download CUDA and CUDNN on your system for faster running of the detection algorithm
• Install Darknet for object detection by following the instructions here
• Download and unzip darknetconfig.zip from the releases tab
• place the files from darknetconfig.zip in the following directories: ./darknet/cfg/yolov4-obj.cfg ./darknet/data/obj.data /darknet/yolov4-obj_best.weights
• Run pip install -r requirements.txt to download all necessary dependencies
• Run streamlit run pii.py to run the detection algorithm, and upload the picture to the website opened.

Contribution

This section provides instructions and details on how to submit a contribution via a pull request. It is important to follow these guidelines to make sure your pull request is accepted.

1. Before choosing to propose changes to this project, it is advisable to go through the readme.md file of the project to get the philosphy and the motive that went behind this project. The pull request should align with the philosphy and the motive of the original poster of this project.
2. To add your changes, make sure that the programming language in which you are proposing the changes should be same as the programming language that has been used in the project. The versions of the programming language and the libraries(if any) used should also match with the original code.
3. Write a documentation on the changes that you are proposing. The documentation should include the problems you have noticed in the code(if any), the changes you would like to propose, the reason for these changes, and sample test cases. Remember that the topics in the documentation is strictly not limited to the topics aforementioned, but are just an inclusion.
4. Submit a pull request via Git etiquettes