pdf_parser

pdf_parser

All the codes related to pdf parsing

The following elements are to be parsed from documents.

1. Documents
   1. Extracting dates from documents
   2. Classification Tags
   3. Extracting Key Entities from documents
      1. Patents
      2. References
      3. Entities
         1. Names
         2. Addresses
         3. Law Firms
         4. Contact Numbers
         5. Emails
   4. Association with Cases

Setting up the workspace environment

1. Creating a virtual environment.
   1. Launch the terminal.

   2. Go to the home directory by typing the following command.

      cd ~
      

      1. Make a new directory Installs using the following command: bash mkdir Installs

   3. Create a virtual environment venv

      python3 -mvenv venv
      

      1. Activate the virtual environment: bash source ~/Installs/venv/bin/activate

2. Setting up apache-tika

   1. Launch terminal

   2. Enter the following command to go to the base directory:

      cd ~
      

      1. Download the apache-tika server using the following command: bash wget https://www.apache.org/dyn/closer.lua/tika/1.28.4/tika-server-1.28.4.jar

   3. Check if java is installed in your machine by running the following command:

      java --version
      

      1. If java is not installed in your local machine, please refer to this documentation.

      2. Running the tika server

      3. Create a new screen called apache-tika

         screen -S apache-tika
         

         1. Enter the following command to run the tika server: bash java -jar {your tika server}

Setting up the code base.

1. Launch the terminal.

2. Enter the following command to go to the base directory:

   cd ~
   

   1. Make a new directory Code by using the following command: bash mkdir Code

3. Pull the current repository by entering the following command:

   git pull gogs@git.fafadiatech.com:harsh/pdf_parser.git
   

   TODO LIST:

   1. Implementing OCR on tika.
   2. Dockerising the whole apache tika with ocr.
   3. Testing the re on the scanned pdfs.