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.

翻译：在本论文中，我们研究了文档布局分析相关的多项任务，包括文本行检测、区域划分及书写载体检测。为此，我们提出了两种遵循不同方法的深度神经模型。我们的目标是提出一种能够应对文档处理特有困难（包括训练数据有限）的目标检测模型。基于此，我们分别提出了像素级检测模型和目标级检测模型。首先，我们设计了一种参数少、预测速度快且能从少量训练数据中获取精确预测掩码的检测模型。我们实现了多数据集的收集与统一化策略，利用这些数据集训练了一个单一行检测模型，该模型对异质文档展现出强大的泛化能力。此外，我们还提出了一种基于Transformer的检测模型。该模型的设计需要重新定义文档图像中的目标检测任务，并研究不同实现方法。基于这一研究，我们提出了一种目标检测策略：通过像素分类按序预测目标外接矩形的坐标。该策略使模型在保持参数精简的同时实现快速检测。最后，在工业场景下，新的未标注数据通常可得。因此，当模型需适应这类新数据时，应尽可能少地提供新的标注样本。对人工标注相关样本的选取对于成功适配至关重要。为此，我们提出了基于不同方法的目标检测置信度估计器。实验表明，这些估计器在优化性能的同时能大幅减少所需标注数据量。