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https://github.com/abixnash/handwritten-recognition

This GitHub repository showcases my "Handwritten Digit Recognition" project, created during my SmartKnower program. It highlights the potent fusion of machine learning and computer vision for precise handwritten digit recognition, streamlining digit-to-digital conversion.
https://github.com/abixnash/handwritten-recognition

deep-learning handwritten-digit-recognition internship internship-project keras keras-tensorflow python

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This GitHub repository showcases my "Handwritten Digit Recognition" project, created during my SmartKnower program. It highlights the potent fusion of machine learning and computer vision for precise handwritten digit recognition, streamlining digit-to-digital conversion.

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# Handwritten Digit Recognition

## Overview

This project demonstrates a basic Handwritten Digit Recognition system using machine learning. It takes an input image of a handwritten digit and predicts the digit using a pre-trained model.

### Features

- Recognizes handwritten digits (0-9).
- Minimal setup required.
- Great for learning the basics of image recognition.

## Getting Started

Follow these simple steps to set up and run the Handwritten Digit Recognition system on your local machine.

### Prerequisites

- Python 3.6 or higher

### Installation

1. Clone this repository:

```bash
git clone https://github.com/AbiXnash/Handwritten-Recognition.git
```

2. Navigate to the project directory:

```bash
cd Handwritten-Recognition
```

### Usage

1. Run the recognition system:

```bash
python recognize.py --input input_image.jpg
```

Replace `input_image.jpg` with the path to your input image containing a handwritten digit (0-9).

2. The recognized digit will be displayed in the console output.

## Contributing

This is a simple example project, but contributions are welcome. Feel free to enhance or extend this project and create a pull request with your improvements.

## License

This project is open-source and available under the MIT License.

## Acknowledgments

- Special thanks to the machine learning community for sharing knowledge and resources.