LayoutLM: Pre-training of Text and Layout for Document Image Understanding
Table 2. LayoutLMBASE (Text + Layout, MVLM) accuracy with different data and epochs on the FUNSD dataset
['[BOLD] # Pre-training Data', '[BOLD] # Pre-training Epochs', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1']
For different data settings, we can see that the overall accuracy is monotonically increased as more epochs are trained during the pre-training step. Furthermore, the accuracy is also improved as more data is fed into the LayoutLM model. As the FUNSD dataset contains only 149 images for fine-tuning, the results confirm that the pre-training of text and layout is effective for scanned document understanding especially with low resource settings.