ashnkumar/sketch-code
Keras model to generate HTML code from hand-drawn website mockups. Implements an image captioning architecture to drawn source images.
What it solves
It converts hand-drawn website wireframes into working HTML code, automating a portion of the front-end development process by translating visual mockups into markup.
How it works
The project uses a deep learning architecture based on image captioning. It employs a CNN encoder to process the image and a LSTM decoder to generate the HTML as if it were a caption for the image. It was trained on a synthetic dataset of 1,700 images.
Who it’s for
Developers and designers who want to experiment with automated mockup-to-code translation using early deep learning techniques.
Highlights
- Uses a CNN-LSTM architecture to treat HTML generation as an image captioning problem.
- Supports batch processing of images to HTML conversion.
- Includes scripts for training the model from scratch or fine-tuning pretrained weights.
- Provides evaluation tools using the BLEU score to measure prediction accuracy.
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