HandwrittenTextFromInput is a Python-based tool that transforms typed English text into images that simulate handwritten script. This project utilizes a dataset from Kaggle, which includes images of handwritten characters and digits. By mapping the ASCII values of input characters to specific images in the dataset, the tool assembles these images to create a seamless handwritten representation of the input text.
- Convert English alphanumeric text to handwritten images: Supports both uppercase and lowercase letters, as well as digits.
- Utilizes a comprehensive Kaggle dataset: Ensures realistic and diverse handwritten styles.
- Customizable output (planned): Allow adjustments such as the spacing between characters and choice of handwriting style, subject to dataset availability. (This feature is currently not implemented)
- Educational Materials: Ideal for creating materials that require handwritten notes, such as worksheets or instructional guides.
- Digital Art Projects: Can be used to generate artistic representations of text for use in digital art.
- User Interface Design: Enhances interfaces by adding a personal touch with handwritten aesthetics, potentially improving user engagement.
Follow these instructions to set up and run English-to-Handwritten-Generator on your local machine.
Before running this project, ensure you have the following installed:
- Python 3.x
- Pandas
- OpenCV
- NumPy
- Matplotlib
- Clone the repository to your local machine:
git clone https://github.com/kushalpatel0265/English-to-Handwritten-Generator.git
- To use this program, run the following command in the terminal:
python handwritten_generator.py
- Enter the text when prompted, and the program will generate an image file with the handwritten representation of the input text.
Input:
Enter a string: Hello
Output:
An image file Random.png will be generated in the project directory, showing "Hello World" in handwritten format.
Contributions to this project are welcome! Please fork the repository and submit a pull request with your features or corrections.
This project is licensed under the MIT License - see the LICENSE file for details.