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Found 4 papers, 3 papers with code
TabIQA: Table Questions Answering on Business Document Images
Table answering questions from business documents has many challenges that require understanding tabular structures, cross-document referencing, and additional numeric computations beyond simple search queries.
An End-to-End Multi-Task Learning Model for Image-based Table Recognition
Most of the previous methods focus on a non-end-to-end approach which divides the problem into two separate sub-problems: table structure recognition; and cell-content recognition and then attempts to solve each sub-problem independently using two separate systems.
Ranked #2 on
Table Recognition
on PubTabNet
Rethinking Image-based Table Recognition Using Weakly Supervised Methods
In this paper, we propose a weakly supervised model named WSTabNet for table recognition that relies only on HTML (or LaTeX) code-level annotations of table images.
Recurrent neural network transducer for Japanese and Chinese offline handwritten text recognition
In this paper, we propose an RNN-Transducer model for recognizing Japanese and Chinese offline handwritten text line images.
