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https://github.com/ishtiuk/employee-registration-automation

A comprehensive project utilizing computer vision, OCR, and generative AI for automating employee registration. It extracts and verifies key details from ID cards and face images, suitable for company registrations, government ID systems, and student databases.
https://github.com/ishtiuk/employee-registration-automation

ai artificial-intelligence automation computer-vision deep-learning deeplearning genai generative-ai information-extraction named-entity-recognition natural-language-processing ner nlp opencv

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A comprehensive project utilizing computer vision, OCR, and generative AI for automating employee registration. It extracts and verifies key details from ID cards and face images, suitable for company registrations, government ID systems, and student databases.

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README 

        
# Employee Registration Automation System



## Overview



The **Employee Registration Automation System** is an advanced project that leverages a combination of computer vision, natural language processing (NLP), and generative AI to automate the employee registration process. This system is designed to streamline and enhance the workflow of registering employees by extracting and verifying crucial information from ID cards and face images. With a robust architecture, this solution is adaptable for various applications, including company employee registration, government NID or passport systems, and student database management in educational institutions.



## Key Features



### 1. Optical Character Recognition (OCR)

- **Technology**: `pytesseract`

- **Description**: The system utilizes OCR to extract text data from uploaded ID card images. This forms the foundational layer for further data processing and information extraction.



### 2. Generative AI for Named Entity Recognition (NER)

- **Technology**: Generative AI

- **Description**: Generative AI is employed to perform Named Entity Recognition (NER), extracting key information such as:

  - Employee ID

  - Full Name

  - Job Position

  - Department

  - Email

  - Phone Number

  - Blood Group

  - Date of Birth (DOB)

  

  This approach goes beyond traditional regex-based methods, excelling in extracting desired information from ambiguous, messy, and unstructured text, making it highly versatile for real-world data inputs.



### 3. Face Verification

- **Techniques**: Advanced computer vision algorithms

- **Description**: The system implements a face verification process that compares the face extracted from the ID card with an uploaded face image. This ensures the authenticity of the registration by confirming that the person being registered matches the ID card provided.



### 4. Duplicate Record Detection

- **Database Operations**: The system checks for existing records to prevent duplicate registrations. This is critical in maintaining the integrity and accuracy of the employee database.



### 5. Logging and Error Handling

- **Logging**: Integrated throughout the application to monitor the process, track errors, and log the face verification status.

- **Error Handling**: Robust mechanisms are in place to manage potential issues in OCR, AI processing, and database operations, ensuring the system's reliability and stability.



## Components



### 1. **OCR Engine**

   - **Tool**: `pytesseract`

   - **Functionality**: Extracts textual information from ID card images, laying the groundwork for further information processing.



### 2. **Generative AI for Information Extraction**

   - **Technology**: Generative AI

   - **Use Case**: Acts as a Named Entity Recognition (NER) model, extracting key details from both structured and unstructured text. While regex worked effectively for well-structured text, generative AI excelled in handling ambiguous and messy text inputs.



### 3. **Face Verification**

   - **Techniques**: Computer vision algorithms to detect, extract, and compare faces.

   - **Use Case**: Ensures that the person being registered matches the ID card provided.



### 4. **Database Operations**

   - **Duplicate Check**: Verifies if the employee ID already exists in the database.

   - **Insertion**: Adds new records to the database if no duplicates are found.



### 5. **Logging and Error Handling**

   - **Logging**: Integrated logging throughout the system to track operations, errors, and face verification statuses.

   - **Error Handling**: Robust error handling to manage OCR, AI processing, and database operations.



## Use Cases



### 1. **Company Employee Registration**

   - **Description**: Streamlines the employee onboarding process by automating the collection and verification of employee details, reducing manual effort and errors.



### 2. **Government ID or Passport Systems**

   - **Description**: Automates the registration and verification process for national identification systems, ensuring accurate data collection and verification.



### 3. **Student Database Management**

   - **Description**: Facilitates the registration and management of student records in educational institutions, automating the data collection process and ensuring data integrity.



## How to Use



### 1. **Manual Registration**

   - Users can manually enter employee details through an intuitive form interface.



### 2. **ID Card Registration**

   - Employees can upload their ID card image along with a face image, and the system will automatically extract and verify their information for registration.



### 3. **View Records**

   - The system includes a utility to view all inserted employee records in the database, providing a comprehensive overview of the registered employees.



## Project Setup



### 1. **Clone the Repository**

   ```bash

   git clone https://github.com/your-username/employee-registration-automation.git

   ```

### 2. **Install Dependencies**

   ```bash

   pip install -r requirements.txt

   ```

### 3. **Run the Application**

   ```bash

   streamlit run app.py

   ```



## Future Enhancements



### 1. **Integration with External APIs**

   - Expand the system to integrate with external employee management APIs, enabling seamless data exchange between different systems.



### 2. **Enhanced Face Verification**

   - Improve the face verification model to handle more challenging image conditions, increasing the accuracy and reliability of the verification process.



### 3. **Multi-language Support**

   - Add support for multiple languages in OCR and information extraction, making the system more versatile and applicable in diverse linguistic settings.



## Conclusion



The **Employee Registration Automation System** is a cutting-edge solution that integrates computer vision and NLP, leveraging generative AI to automate and enhance the employee registration process. This system is not only efficient and accurate but also highly adaptable for various registration systems, ensuring precise data management across different domains.