This paper presents a model that automatically finds and understands tables from scanned documents—tasks that are essential for quick of using information in many fields. The model uses a mix of two advanced techniques, CornerNet and Faster R-CNN, to accurately locate tables and understand their layout. Tests on standard datasets, IIIT-AR-13K and SciTSR, show that this model performs better than previous ones, making it very good at dealing with tables that have complicated designs or are in documents with a lot of detail. The success of this model marks a step forward in making document analysis more automated. It makes it easier to turn complex scanned document containing tables into data that computers can read.
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Ngubane et al. (2024) studied this question.