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The unambiguous structure representation of tabular data for recognition.

Fan Yang1, Junwen Tan1, Tianshui Chen2

  • 1School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 15, 2025
PubMed
Summary

This study introduces the Table Structure Graph (TSG), an unambiguous representation for complex tabular data recognition. TSG enhances accuracy and efficiency in converting image tables to machine-readable formats.

Keywords:
Definition of structureRepresentation of structureStructure of tabular dataStructure recognitionStructure recognition datasetTabular dataUnambiguous representation

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Tabular data recognition from images is challenging due to complex structures like cell spanning and nested spanning.
  • Existing representations suffer from ambiguity, hindering accurate structure recognition and degrading performance.
  • Developing machine-readable representations for diverse and complex table structures is crucial for data processing.

Purpose of the Study:

  • To propose an unambiguous representation for complex tabular data structures.
  • To introduce a new dataset for advancing complex table structure recognition.
  • To improve the accuracy and efficiency of converting image-based tables into machine-readable formats.

Main Methods:

  • Introduced the Table Structure Graph (TSG) as an unambiguous representation for tabular data.
  • Developed the Complex Table (CmpTab) dataset featuring diverse and complex table structures.
  • Provided mathematical proof of TSG's unambiguity via grid-geometry and graph mapping.
  • Adapted TSG to existing models and proposed the tsg2ms conversion algorithm.

Main Results:

  • TSG demonstrated enhanced accuracy and efficiency in tabular structure recognition compared to existing methods.
  • The CmpTab dataset facilitates research on complex table structure recognition.
  • TSG achieved a 100% transfer rate on the CmpTab dataset, confirming its unambiguity.
  • The tsg2ms algorithm enables efficient conversion of TSG back to tabular data.

Conclusions:

  • The Table Structure Graph (TSG) offers an unambiguous and effective representation for complex tabular data.
  • TSG significantly improves the performance of tabular structure recognition models.
  • The proposed dataset and conversion algorithm facilitate practical applications of advanced tabular data recognition.