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Evaginating scientific charts: Recovering direct and derived information encodings from chart images

Abstract

Recovering information encodings from charts is to extract textual and graphical information from the diverse representations of charts to aid various applications that entail the hidden and directly available information. This paper proposes an end-to-end relationship-aware model for evaginating scientific charts for which an automated encoder-decoder framework is adopted. The framework integrates the CNN-LSTM model to extract direct and derived data by the influence of semantic relationships between the textual and graphical components. Semantic relationship makes the model robust towards diverse chart structures and orientations. Entity relationship-aware module extracts and builds relations amid textual-graphical components of the charts, and decodes the hidden data from the chart images. Our framework is one of its kind to recover data encoding from chart images based upon their inter-object semantic relationships, to the best of our knowledge. Model is tested upon public datasets, obtaining more than 97% accuracy compared with benchmark systems.

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Correspondence to Prerna Mishra.

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Mishra, P., Kumar, S. & Chaube, M.K. Evaginating scientific charts: Recovering direct and derived information encodings from chart images. J Vis (2021). https://doi.org/10.1007/s12650-021-00800-z

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Keywords

  • Information extraction
  • Reverse engineering
  • Charts
  • Relation graphs
  • Direct and derived data