Image2Tikz: Neural Network-Based TikZ Code Generation

Type: MA thesis

Status: open

Supervisors: Linda-Sophie Schneider

Work description
This thesis aims to develop Image2Tikz, a tool that uses neural networks to transform images into TikZ code by segmenting objects, estimating distances between them, and then translating this information into TikZ. Starting with simple TikZ images and progressing to more complex, hand-drawn versions, the tool will be refined to handle increasing noise levels and complexities. The performance will be evaluated by comparing the generated TikZ code against the original images.

The following questions should be considered:

  • How can neural networks be effectively trained for tikz object segmentation and distance estimation in images?
  • What techniques can be used to translate segmented objects and their relative distances into TikZ code?
  • How does the introduction of noise and complexity in images affect the tool’s accuracy and reliability?
  • What strategies can improve the tool’s performance on more complex, hand-drawn images?

 

Prerequisites
Candidates should have a strong foundation in machine learning, particularly in neural networks, with practical experience in Python and familiarity with PyTorch. Skills in image processing and an understanding of LaTeX, especially TikZ, are desirable. The ability to work independently and creatively solve problems is essential.

Please include your transcript of record with your application.