Example of segmentations made with Doc-UFCN models: detection of illustrations on the left, initials, pages and rubrications in the middle and text lines on the right.

During the International Conference on Pattern Recognition (ICPR2020) held in 2021, Teklia and the LITIS (University of Rouen-Normandy) presented the Doc-UFCN model for automatic segmentation of document images in the paper Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks. This model, based on a U-Net architecture, is designed to perform different Document Layout Analysis (DLA) tasks like text line detection and page segmentation. Doc-UFCN has shown very good performances on different tasks and a reduced inference time, which is why it is now used in most of Teklia's projects.

The Doc-UFCN library is now available on Pypi and allows to apply trained models to document images. It can be used by anyone that has an already trained Doc-UFCN model and want to easily apply it to document images. With only a few lines of code, the trained model is loaded, applied to an image and the detected objects along with some visualizations are obtained.

We also provide an open-source model that detects physical pages on a document image.


Given an input image, the detected objects are returned as a list of confidence scores and polygons coordinates. The library can also return the raw probabilities and two visualizations: a mask of the detected objects and an overlap of the detected objects on the input image.

Visualizations obtained by the library with the generic page segmentation model.