IDSDK - Windows

ID Document Recognition Windows Server SDK

Our SDK is fully on-premise, processing all happens on hosting server and no data leaves your server.
14,000+ document templates covering IDs issued in 250+ countries and territories.
Support of 140+ languages and special characters via sophisticated neural networks.

Introduction

Welcome to the MiniAiLive ID Document Recognition SDK! This SDK provides powerful tools for recognizing and extracting information from ID documents. The SDK 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 mobile or server SDK. Try it out today!

Installation

Prerequisites

Python 3.6+
Windows
CPU: 2 cores or more
RAM: 4GB or more

Installation Steps

Download the ID Document Recognition Windows Server Installer
Download the Server installer for your operating system from the following link:
Download the On-premise Server Installer
Install the On-premise Server
Run the installer and follow the on-screen instructions to complete the installation

Request License and Update Run MIRequest.exe file to generate a license request file. You can find it here. Open it, generate a license request file, 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.

C:\Program Files\MiniAiLive\MiniAiLive-ID-Server

Verify Installation After installation, verify that the On-premise Server is correctly installed by checking the task manager

API Reference

Endpoint

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

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

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

Raw Data:

JSON Format:

{
    "image": "--base64 image data here--"
}

Other available endpoints here.

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>

Response

The API returns a JSON object with the liveness result of the input face image. Here is an example response

Testing API

Gradio Demo

We have included a Gradio demo to showcase the capabilities of our MiniAiLive 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

Install Gradio:

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

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

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)