  In this thesis, we study multiple tasks related to document layout analysis
such as the detection of text lines, the splitting into acts or the detection
of the writing support. Thus, we propose two deep neural models following two
different approaches. We aim at proposing a model for object detection that
considers the difficulties associated with document processing, including the
limited amount of training data available.
  In this respect, we propose a pixel-level detection model and a second
object-level detection model. We first propose a detection model with few
parameters, fast in prediction, and which can obtain accurate prediction masks
from a reduced number of training data. We implemented a strategy of collection
and uniformization of many datasets, which are used to train a single line
detection model that demonstrates high generalization capabilities to
out-of-sample documents.
  We also propose a Transformer-based detection model. The design of such a
model required redefining the task of object detection in document images and
to study different approaches. Following this study, we propose an object
detection strategy consisting in sequentially predicting the coordinates of the
objects enclosing rectangles through a pixel classification. This strategy
allows obtaining a fast model with only few parameters.
  Finally, in an industrial setting, new non-annotated data are often
available. Thus, in the case of a model adaptation to this new data, it is
expected to provide the system as few new annotated samples as possible. The
selection of relevant samples for manual annotation is therefore crucial to
enable successful adaptation. For this purpose, we propose confidence
estimators from different approaches for object detection. We show that these
estimators greatly reduce the amount of annotated data while optimizing the
performances.
