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Extracting tables from documents is a crucial task in any document conversionpipeline. Recently, transformer-based models have demonstrated thattable-structure can be recognized with impressive accuracy usingImage-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table,such models predict a sequence of tokens (e.g. in HTML, LaTeX) which representthe structure of the table. Since the token representation of the tablestructure has a significant impact on the accuracy and run-time performance ofany Im2Seq model, we investigate in this paper how table-structurerepresentation can be optimised. We propose a new, optimised table-structurelanguage (OTSL) with a minimized vocabulary and specific rules. The benefits ofOTSL are that it reduces the number of tokens to 5 (HTML needs 28+) andshortens the sequence length to half of HTML on average. Consequently, modelaccuracy improves significantly, inference time is halved compared toHTML-based models, and the predicted table structures are always syntacticallycorrect. This in turn eliminates most post-processing needs.

Optimized Table Tokenization for Table Structure Recognition