AI-Optical-Character-Recognition (AI-OCR): Extracting data from images

This repository is the backend code for a tool with which you can extract data from images using visual LLMs. The frontend code (using streamlit) can be found here: AI-OCR-Frotend.

Table of Contents

• Installation
• Usage
• Example
• License

Installation

To use the AI-OCR tool, it is best if you install this repository for the backend, as well as the frontend repository, i.e. follow these steps:

1. Clone this repository for the backend

git clone https://github.com/jWinman91/AI-OCR.git
cd ai-ocr

2. Install the required dependencies for the backend:

pip install -r requirements.txt

3. Pull and run the coachdb docker file with the following command:

docker run -e COUCHDB_USER=admin -e COUCHDB_PASSWORD=JensIsCool -p 5984:5984 -d --name config_db couchdb:latest

3. Clone the frontend repository

git clone https://github.com/jWinman91/AI-OCR-Frontend.git
cd ai-ocr-frondend

3. Install the required dependencies for the frontend:

pip install -r requirements.txt

Usage

You can then start the backend by running:

python app.py $IP_ADDRESS

Make sure that the docker container for the coachdb is running.

Since, the backend uses fastapi, you could now try it out via the fastapi docs by going to $IP_ADDRESS:5000/docs.

But you can also start the frontend now by running:

chmod +x start_up.sh
./start_up.sh

from within the cloned frontend repository.

A streamlit window will automaticall open in your browser. Within the web application you'll then find two pages on the sidebar:

• AI-OCR: Webpage for running the actual optical character recognition
• Model Configurations: Subpage for configuring the models (e.g. ChatGPT, Llava, ...)

Example

Acknowledgments

• Hugging Face - Framework for working with state-of-the-art natural language processing models.