PubLayNet: largest dataset ever for document layout analysis
TABLE III: MAP @ IOU [0.50:0.95] of the F-RCNN and the M-RCNN models on our development and testing sets. M-RCNN shows a small advantage over F-RCNN.
['Category', 'Dev F-RCNN', 'Dev M-RCNN', 'Test F-RCNN', 'Test M-RCNN']
The evaluation metric is the mean average precision (MAP) @ Both models can generate accurate (MAP >0.9) document layout, where M-RCNN shows a small advantage over F-RCNN. The models are more accurate at detecting tables and figures than texts, titles, and lists. We think this is attributed to more regular shapes, more distinctive differences from other categories, and lower rate of erroneous annotations in the training set. The models perform worst on titles, as titles are usually much smaller than other categories and more difficult to detect.