Abstract Historical document analysis plays a crucial role in understanding and preserving our past. However, this task is often hindered by challenges such as limited annotated training data and the diverse nature of historical handwritten documents. In this paper, we explore the potential of self-supervised learning (SSL) in historical document analysis, with a particular focus on historical handwritten document segmentation, to overcome the need for extensive annotated data while enhancing efficiency and robustness. We present an overview of SSL methods suitable for historical document analysis and discuss their potential applications and benefits. Furthermore, we present an approach for SSL in the document domain, considering various setups, augmentations, and resolutions. We also provide experimental results that demonstrate its feasibility and effectiveness. Our findings indicate that most document segmentation tasks can be effectively addressed using SSL features, highlighting the potential of SSL to advance historical document analysis and pave the way for more efficient and robust document processing workflows.

Dialogue act (DA) recognition is usually realized from a speech signal that is transcribed and segmented into text. However, only a little work in DA recognition from images exists. Therefore, this paper concentrates on this modality and presents a novel DA recognition approach for image documents, namely comic books. To the best of our knowledge, this is the first study investigating dialogue acts from comic books and represents the first steps to building a model for comic book understanding. The proposed method is composed of the following steps: speech balloon segmentation, optical character recognition (OCR), and DA recognition itself. We use YOLOv8 for balloon segmentation, Google Vision for OCR, and Transformer-based models for DA classification. The experiments are performed on a newly created dataset comprising 1,438 annotated comic panels. It contains bounding boxes, transcriptions, and dialogue act annotation. We have achieved nearly 98% average precision for speech balloon segmentation and exceeded the accuracy of 70% for the DA recognition task. We also present an analysis of dialogue structure in the comics domain and compare it with the standard DA datasets, representing another contribution of this paper.

This paper presents a novel method for grid detection in historical maps. The approach is based on Hough transform accompanied with a sophisticated post-processing. They are applied to detect the grid that consists of graticule lines. It works without any training and does not require any annotated data. The proposed approach is very efficient in detecting the rectangular grid and the intersection points as shown in the international "MapSeg" segmentation competition, where it won the Task 3 with a significant margin. The robustness of the proposed method has been demonstrated by evaluating on another dataset composed of significantly different cadastral map images with excellent results.

International audience