pawanbgs0/doceye

DocEye is an advanced FastAPI project built using Convolutional Neural Networks (CNN) to accurately distinguish between faces and signatures in uploaded images. This powerful API enables seamless integration into various applications and systems, providing efficient document analysis and verification capabilities.

DocEye

DocEye is an advanced FastAPI project built using Convolutional Neural Networks (CNN) to accurately distinguish between faces and signatures in uploaded images. This powerful API enables seamless integration into various applications and systems, providing efficient document analysis and verification capabilities.

DocEye: Face and Signature Recognition API

Key Features:

• FastAPI framework for high-performance API handling and scalability.
• Utilizes Convolutional Neural Networks (CNN) for accurate image classification.
• Supports image upload for real-time processing and analysis.
• Provides detailed classification results for faces and signatures.
• Easy integration with existing systems and applications.

How It Works:

• Upload an image containing a face or a signature using the API endpoint.
• DocEye preprocesses the image and extracts relevant features.
• The CNN model performs advanced analysis and classifies the image as a face or a signature.
• The API responds with the prediction, indicating whether the image contains a face or a signature.

Benefits:

• Accurately differentiate between faces and signatures in uploaded images.
• Streamline document processing workflows with automated image classification.
• Enhance document analysis and verification processes with advanced machine learning techniques.
• Seamlessly integrate the DocEye API into your applications or systems.
• Improve efficiency, reduce manual effort, and enhance document management tasks.

INSTALLATION

1. Clone the repository : git clone https://github.com/istebsharul/DocEye.git

2. Download the Model Link : https://drive.google.com/file/d/1k5m8gF1dpZTdeMsv2VnVefQbUIGwsf_f/view?usp=sharing

3. Install the required dependencies : pip install -r requirements.txt

Steps to Use the model

1. After installing all the dependencies.
2. Run the main.py from terminal cmd: uvicorn main:app --reload
3. open Local Host : http://127.0.0.1:8000
4. Use Swagger for UI by appending /docs after http://127.0.0.1:8000/docs
5. Go to Post Section Upload Image and you will get the Result.