LayoutLM: Pre-training of Text and Layout for Document Image Understanding
Table 4. Model accuracy (Precision, Recall, F1) on the SROIE dataset
['[BOLD] Modality', '[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1', '[BOLD] #Parameters']
We evaluate the receipt understanding task using the SROIE dataset. As we only test the performance of the Key Information Extraction task in SROIE, we would like to eliminate the effect of incorrect OCR results. Therefore, we pre-process the training data by using the ground truth OCR and run a set of experiments using the baseline models (BERT & RoBERTa) as well as the LayoutLM model. The results show that the LayoutLMLARGE model trained with 11M document images achieve an F1 score of 0.9524, which is significantly better than the first place in the competition leaderboard. This result also verifies that the pre-trained LayoutLM not only performs well on the in-domain dataset (FUNSD) but also outperforms several strong baselines on the out-of-domain dataset like SROIE.