[dkatz23238]

As a developer who has been building IDP solutions I can assert that although this model is a lot larger (more weights) than a Graph Neural Network on OCR tokens, industry standard before transformers, it outperforms given enough data. Depending on how heterogenous the data is usually 200 documents can reach human levels of accuracy on documents, scoring by levenshtein ratio.

Smaller graph models could get away with using less data. The problem that the "traditional" approach had is the the quality of the OCR was the bottleneck for overall model performance. It amazes me how this problem shifted from a node classification problem to a image to text problem.

Training on CPU was possible with GCN but not with Donut.