If you’re intrigued by the potential of deep learning in translating rendered images into LaTeX or HTML source code, you are in the right place. This article delves into the TensorFlow implementation of the HarvardNLP paper, “What You Get Is What You See: A Visual Markup Decompiler”. Here, we will break down the steps necessary to execute this powerful system, as well as troubleshoot common issues you may encounter.

Understanding the Concept

Imagine you’re in an art gallery where every painting is a mathematical or scientific formula rendered in beautiful imagery. Your task is to describe each painting in words (in this case, LaTeX). The im2latex project uses deep learning to automate this transformation, taking an image as input and returning the textual description (LaTeX code) that generated it. This implementation seeks to unravel the visual into comprehensible code.

Prerequisites

Before diving into the implementation, let’s gather the necessary tools and libraries:

• TensorFlow – The core framework for our implementation.
• Python – For preprocessing tasks.
• Pillow – For image processing.
• NumPy – For handling data in arrays.
• Node.js – Optional, but useful for preprocessing. You can find it here.
• Pdflatex and ImageMagick – Tools for rendering LaTeX images. Install Pdflatex and ImageMagick.
• Webkit2png – Used for rendering HTML during evaluation. Installation instructions can be found here.

Preprocessing the Data

Preprocessing is crucial for optimizing the model’s performance. Follow these instructions closely:

1. Download the training data from this link and extract it into your source folder.
2. Run the following command to crop the formula area:

   cd im2markup
       python scripts/preprocessing/preprocess_images.py --input-dir ../formula_images --output-dir ../images_processed

3. Normalize the LaTeX formulas using:

   python scripts/preprocessing/preprocess_formulas.py --mode normalize --input-file ../im2latex_formulas.lst --output-file formulas.norm.lst

4. Prepare the training, validation, and test datasets by filtering images and formulas as needed. Use the following commands:

   python scripts/preprocessing/preprocess_filter.py --filter --image-dir ../images_processed --label-path formulas.norm.lst --data-path ../im2latex_train.lst --output-path train.lst
       python scripts/preprocessing/preprocess_filter.py --filter --image-dir ../images_processed --label-path formulas.norm.lst --data-path ../im2latex_validate.lst --output-path validate.lst
       python scripts/preprocessing/preprocess_filter.py --no-filter --image-dir ../images_processed --label-path formulas.norm.lst --data-path ../im2latex_test.lst --output-path test.lst

5. Generate the vocabulary:

   python scripts/preprocessing/generate_latex_vocab.py --data-path train.lst --label-path formulas.norm.lst --output-file latex_vocab.txt

Training the Model

Once you have preprocessed your data, it’s time to train the model:

python attention.py

Default hyperparameters will be applied, which you can modify as needed.

Testing the Implementation

To make predictions based on the validation or test datasets, call the predict() function located in attention.py:

predict()

Finally, visualize your results with the Predict.ipynb notebook.

Troubleshooting Tips

If you encounter issues during the implementation, here are some troubleshooting ideas:

• Error in data preprocessing: Ensure all paths and commands are correctly specified.
• Model training is unsuccessful: Adjust the batch size or learning rate.
• Rendering issues: Verify installations of Pdflatex, ImageMagick, and Webkit2png.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

This TensorFlow implementation of the im2latex project serves as an exciting exploration into how deep learning can translate visual information into textual descriptions effectively. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.