This project aims to develop a coding language that can be scanned from an image. The idea is to combine image processing techniques with natural language processing (NLP) to interpret code elements from images. For instance, the system could detect circles representing code blocks and then analyze the text wrapping around these circles to identify programming constructs.
- Image Preprocessing: Enhance image quality and remove noise to prepare it for further processing.
- Circle Detection: Utilize the Hough Circle Transform to detect circles in the image, potentially representing key code elements.
- Text Extraction: Employ Optical Character Recognition (OCR) to extract text from regions wrapping around the detected circles.
- Pattern Recognition: Analyze the extracted text to recognize patterns associated with programming constructs.
- Syntax and Semantics: Implement grammar and syntax rules to interpret detected patterns and generate meaningful code.
- Code Generation/Interpretation: Translate recognized patterns into executable code or directly interpret patterns.
- Testing and Validation: Thoroughly test the system with various input images and code patterns to ensure accuracy.
- Error Handling: Implement mechanisms to handle errors arising from image processing, OCR, or pattern recognition.
- Python 3.x
- OpenCV (for image processing)
- Tesseract OCR (for text extraction)
- Other necessary libraries (list them here)
- Clone this repository:
git clone https://github.com/yourusername/WeaveCompiler.git
cd weavecompiler
- Install the required dependencies:
pip install -r requirements.txt
- Download and install Tesseract OCR:
-
Place your input image in the
input_imagesdirectory. -
Run the main script:
python main.py
- Check the generated output in the
output_codedirectory.
In this example image, circles indicate code blocks, and the text wrapping around them represents code constructs.
- Accuracy may vary based on image quality and complexity of code elements.
- Handling non-standard code patterns might be challenging.
- Initial setup and installation might be complex due to dependencies.
- Advanced text parsing techniques for more accurate pattern recognition.
- Improved error handling and user feedback.
- User interface for uploading images and viewing generated code.
Contributions are welcome! Please feel free to open issues or submit pull requests.
This project is licensed under the MIT License.