👏 We have published the Face Livness Detection, Face Recognition SDK and ID Card Recognition SDK for the server.
The demo project demonstrates the server-based recognition capabilities for ID cards, passports, and driver's licenses.
At the core of this project lies the ID Card Recognition SDK, which has been developed to provide comprehensive support for recognizing ID cards, passports, and driver's licenses from over 180 countries.
The demo is integrated with KBY-AI's ID Card Recognition Server SDK.
For other solutions, please explore the following:
You can test the SDK using images from the following URL: http://18.221.33.238:9000/
To test the API, you can use Postman. Here are the endpoints for testing:
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Test with an image file: Send a POST request to http://18.221.33.238:8082/idcard_recognition
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Test with a base64-encoded image: Send a POST request to http://18.221.33.238:8082/idcard_recognition_base64
You can download the Postman collection to easily access and use these endpoints. click here
This project uses KBY-AI's Face Recognition Server SDK, which requires a license per machine.
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The code below shows how to use the license: https://github.com/kby-ai/IDCardRecognition-Docker/blob/9f8138fa83d39a80a95e71b52048dbfc6579558c/app.py#L14-L25
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In order to request the license, please provide us with the machine code obtained from the "getMachineCode" function.
Please contact us:
Email: [email protected] Telegram: @kbyai WhatsApp: +19092802609 Skype: live:.cid.66e2522354b1049b
- CPU: 2 cores or more (Recommended: 2 cores)
- RAM: 4 GB or more (Recommended: 8 GB)
- HDD: 4 GB or more (Recommended: 8 GB)
- OS: Ubuntu 20.04 or later
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Clone the project:
git clone https://github.com/kby-ai/IDCardRecognition-Docker.git -
Download the model from Google Drive: click here
cd IDCardRecognition-Docker wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1fmTUG7a9IoMA8QiXR9A0xf3Cr6D5UkdC' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1fmTUG7a9IoMA8QiXR9A0xf3Cr6D5UkdC" -O data.zip && rm -rf /tmp/cookies.txt unzip data.zip -
Build the Docker image:
sudo docker build --pull --rm -f Dockerfile -t kby-ai-idcard:latest . -
Run the Docker container:
sudo docker run -v ./license.txt:/root/kby-ai-idcard/license.txt -p 8082:8080 kby-ai-idcard -
Send us the machine code and replace the license.txt file you received. Then, run the Docker container again.
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To test the API, you can use Postman. Here are the endpoints for testing:
Test with an image file: Send a POST request to http://{xx.xx.xx.xx}:8082/idcard_recognition
Test with a base64-encoded image: Send a POST request to http://{xx.xx.xx.xx}:8081/idcard_recognition_base64
You can download the Postman collection to easily access and use these endpoints. click here
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Setup Gradio Ensure that you have the necessary dependencies installed.
Gradio requires Python 3.6 or above.
You can install Gradio using pip by running the following command:
pip install gradio -
Run the demo Run it using the following command:
cd gradio python demo.py -
You can test within the following URL:
http://127.0.0.1:9000
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Step One
First, obtain the machine code for activation and request a license based on the machine code.
machineCode = getMachineCode() print("machineCode: ", machineCode.decode('utf-8')) -
Step Two
Next, activate the SDK using the received license.
setActivation(license.encode('utf-8'))If activation is successful, the return value will be SDK_SUCCESS. Otherwise, an error value will be returned.
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Step Three
After activation, call the initialization function of the SDK.
initSDK()If initialization is successful, the return value will be SDK_SUCCESS. Otherwise, an error value will be returned.
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ID Card Recognition
The SDK provides a single API for ID card recognition.
The function can be used as follows:
ret = idcardRecognition(base64_image.encode('utf-8'))The function accepts only one parameter, which should be the base64-encoded format of the image (e.g., JPG, PNG, etc.).
If the recognition is successful, the function will return a JSON-formatted string containing the recognized information. In case of failure, the return value will be NULL.