Reading order for pseudo-OCR pre-training task

I would like to train the base model for a few more epochs on the pre-training pseudo-OCR task using a custom dataset. In what reading order should the individual words of the document image be passed to the model? The Donut paper states: > The model is trained to read all texts in the image in reading order (from top-left to bottom-right, basically). [...] This task can be interpreted as a pseudo-OCR task. What does "top-left to bottom-right" mean for multi-column text? For instance, consider the attached dummy document with one heading and two text columns:  Should the document be transcribed as: - _Word1 Col1w1 Col1w2 Col2w1 Col2w2_, or - _Word1 Col1w1 Col2w1 Col1w2 Col2w2_ ? I imagine that any dataset used for the pre-training pseudo-OCR task should adopt the same reading order policy as the pe-trained Donut base model. Unfortunately, I am not able to find any information of the exact implementation of "top-left to bottom-right", neither in the paper, the paper supplement, nor the source code. After all, "top-left to bottom-right" can be interpreted in different ways: - top-to-bottom, left-to-right - left-to-right, top-to-bottom - clustering of words into text blocks to mimic semantically meaningful text paragraphs - etc.