ID Document Recognition Docker

Welcome to the MiniAiLive!

Welcome to the ID Document Recognition Docker! This Docker provides powerful tools for recognizing and extracting information from ID documents. The Docker is available for both Windows and Linux platforms and includes an API for integration.

Reduce drop-off and boost conversions with ID scanning and verification solutions. Quickly and securely capture, extract, and verify data from diverse ID cards, passports, driver’s licenses, and other documents with our proven, AI-first approach. Designed to fit seamlessly together, our technology can be integrated as a fully-bundled identity document verification solution or as separate modules via developer-friendly. Try it out today!

Note

• Our SDK is fully on-premise, processing all happens on hosting server and no data leaves server.
• 10,000+ document templates covering IDs issued in 200+ countries and territories.
• Support of 100+ languages and special characters via sophisticated neural networks.

Table of Contents

• Installation Guide
• API Details
• Gradio Demo
• Python Test API Example

IDSDK Docker Installation Guide

Prerequisites

• Python 3.6+
• Linux
• CPU: 2 cores or more
• RAM: 8 GB or more

Installation Steps

1. Download the ID Document Recognition Docker Image:

   Download the Server Docker Image from the following link:

   Download the On-premise Server Installer

2. Install the On-premise Docker Server:

   Run the Docker Image and follow the on-screen instructions to complete the installation. Go to the Download folder and run this command.

   $ cd Download
   $ sudo docker load -i MiniAiLive-IDSDK-DockerImg.tar
   

   

   You can refer our Documentation here. https://docs.miniai.live

3. Request License and Update:

   You can generate the License Request file by using this command:

   $ sudo chmod 777 ./MiRequest_IDSDK
   $ sudo ./MiRequest_IDSDK request /home/ubuntu/Download/trial_key.miq
   

   
   Then you can see the license request file on your directory, and send it to us via email or WhatsApp. We will send the license based on your Unique Request file, then you can upload the license file to allow to use. Refer the below images.

   $ sudo docker run -d --privileged -v /home/ubuntu/Downloads/trial_key.mis:/var/idsdk.license -p {your_port}:8082 mini-idsdk-server
   

   

4. Verify Installation:

   After installation, verify that the On-premise Server is correctly installed by using this command:

   $ netstat -tnpl
   

   If you can see opened your port correctly, the server has been installed successfully. Refer the below image.

   

IDSDK API Details

Endpoint

• POST http://127.0.0.1:8082/api/check_id ID Document Recognition API

• POST http://127.0.0.1:8082/api/check_id_base64 ID Document Recognition API

• POST http://127.0.0.1:8082/api/check_credit Bank & Credit Card Reader API

• POST http://127.0.0.1:8082/api/check_credit_base64 Bank & Credit Card Reader API

• POST http://127.0.0.1:8082/api/check_mrz MRZ & Barcode Recognition API

• POST http://127.0.0.1:8082/api/check_mrz_base64 MRZ & Barcode Recognition API

Request

• URL: http://127.0.0.1:8082/api/check_id

• Method: POST

• Form Data:

  • image: The image file (PNG, JPG, etc.) to be analyzed. This should be provided as a file upload.

• URL: http://127.0.0.1:8082/api/check_id_base64

• Method: POST

• Raw Data:

  • JSON Format: { "image": "--base64 image data here--" }

Response

The API returns a JSON object with the recognized details from the ID document. Here is an example response:

Gradio Demo

We have included a Gradio demo to showcase the capabilities of our ID Document Recognition SDK. Gradio is a Python library that allows you to quickly create user interfaces for machine learning models.

How to Run the Gradio Demo

1. Install Gradio:

   First, you need to install Gradio. You can do this using pip:

   git clone https://github.com/MiniAiLive/ID-DocumentRecognition-Docker.git
   pip install -r requirement.txt
   cd gradio
   

2. Run Gradio Demo:

   python app.py
   

Python Test API Example

To help you get started with using the API, here is a comprehensive example of how to interact with the ID Document Recognition API using Python. You can use API with another language you want to use like C++, C#, Ruby, Java, Javascript, and more

Prerequisites

• Python 3.6+
• requests library (you can install it using pip install requests)

Example Script

This example demonstrates how to send an image file to the API endpoint and process the response.

import requests

# URL of the web API endpoint
url = 'http://127.0.0.1:8082/api/check_id'

# Path to the image file you want to send
image_path = './test_image.jpg'

# Read the image file and send it as form data
files = {'image': open(image_path, 'rb')}

try:
    # Send POST request
    response = requests.post(url, files=files)

    # Check if the request was successful
    if response.status_code == 200:
        print('Request was successful!')
        # Parse the JSON response
        response_data = response.json()
        print('Response Data:', response_data)
    else:
        print('Request failed with status code:', response.status_code)
        print('Response content:', response.text)

except requests.exceptions.RequestException as e:
    print('An error occurred:', e)

Request license

Feel free to Contact US to get a trial License. We are 24/7 online on WhatsApp.

Face & IDSDK Online Demo, Resources

Our Products

Face Recognition SDK

Face Liveness Detection SDK

ID Document Recognition SDK

ID Document Liveness Detection SDK

Web & Desktop Demo

About MiniAiLive

MiniAiLive is a leading AI solutions company specializing in computer vision and machine learning technologies. We provide cutting-edge solutions for various industries, leveraging the power of AI to drive innovation and efficiency.

Contact US

For any inquiries or questions, please contact us on WhatsApp.