Reproduce “Optimized Table Tokenization for Table Structure Recognition” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Maksym Lysak, Ahmed Nassar, Nikolaos Livathinos, Christoph Auer, Peter Staar
IBM Research introduces an Optimized Table Structure Language (OTSL) to improve Image-to-Markup-Sequence models for table structure recognition. This specialized language, with its minimal vocabulary and strict syntax, enables models to achieve up to 2x faster inference times and enhanced accuracy compared to HTML-based approaches, while ensuring syntactically correct outputs.
Key insights
- Standard markup languages like HTML are inefficient for machine learning models in TSR due to large vocabularies, long sequences, and complex syntax.
- A purpose-built, concise language with strict, backward-looking syntax can drastically improve model efficiency and output quality.
- Optimizing data representation can be as impactful as architectural improvements for deep learning sequence generation tasks.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.