• Priti P Kohale
  • Shailija Sharma
Keywords: Big Data Analytics, Supply Chain Optimization, Image Processing, Table Structure Recognition, Table Cell Type Classification


The essential details of ICT devices are frequently distilled into large tabular data sets that are distributed throughout supply chains as a result of the features of Information and Communications Technology (ICT) goods. With the explosion of electronic assets, it is crucial to automatically analyse tabular structures. We develop a Table Structure Recognition (TSR) work and a Table Cell Type Classification (CTC) task to convert the tabular data in electronic documents into a machine-interpretable format and give layout and semantic information for information extraction and interpretation. For the TSR job, complicated table structures are represented using a graph. Table cells are divided into three groups—Header, Attribute, and Data—based on how they work for the CTC job. Then, utilising the text modal and picture modal characteristics, we provide a multi-task model to accomplish the two tasks concurrently. Our test findings demonstrate that, using the ICDAR2013 and UNLV datasets, our suggested strategy can beat cutting-edge approaches.


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How to Cite
Kohale, P. P., & Sharma, S. (2023). HANDLING BIG TABULAR DATA OF ICT SUPPLY CHAINS: A MULTI-TASK, MACHINE-INTERPRETABLE APPROACH. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 9(2), 1-8. https://doi.org/10.33130/AJCT.2023v09i02.001

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