LayoutLM: Pre-training of Text and Layout for Document Image Understanding
Table 1. Model accuracy (Precision, Recall, F1) on the FUNSD dataset
['[BOLD] Modality', '[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1', '[BOLD] #Parameters']
Form Understanding We evaluate the form understanding task on the FUNSD dataset. We compare the LayoutLM model with two SOTA pre-trained NLP models: BERT and RoBERTa The BERT BASE model achieves 0.603 and while the LARGE model achieves 0.656 in F1. Compared to BERT, the RoBERTa performs much better on this dataset as it is trained using larger data with more epochs. Due to the time limitation, we present 4 settings for LayoutLM, which are 500K document pages with 6 epochs, 1M with 6 epochs, 2M with 6 epochs as well as 11M with 2 epochs. It is observed that the LayoutLM model substantially outperforms existing SOTA pre-training baselines. With the BASE architecture, the LayoutLM model with 11M training data achieves 0.7866 in F1, which is much higher than BERT and RoBERTa with the similar size of parameters. In addition, we also add the MDC loss in the pre-training step and it does bring substantial improvements on the FUNSD dataset. Finally, the LayoutLM model achieves the best performance of 0.7927 when using the text, layout, and image information at the same time.