Dynamic instance generation for few-shot handwritten document layout segmentation (short paper)

Historical handwritten document analysis is an important activity to retrieve information about our past. Given that this type of process is slow and time-consuming, the humanities community is searching for new techniques that could aid them in this activity. Document layout analysis is a branch of machine learning that aims to extract semantic informations from digitised documents. Here we propose a new framework for handwritten document layout analysis that differentiates from the current state-of-the-art by the fact that it features few-shot learning, thus allowing for good results with little manually labelled data and the dynamic instance generation process. Our results were obtained using the DIVA - HisDB dataset

Is ImageNet Always the Best Option? An Overview on Transfer Learning Strategies for Document Layout Analysis

Semantic segmentation models have shown impressive performance in the context of historical document layout analysis, but their effectiveness is reliant on having access to a large number of high-quality annotated images for training. A popular approach to address the lack of training data in other domains is to rely on transfer learning to transfer the knowledge learned from a large-scale, general-purpose dataset (e.g. ImageNet) to a domain-specific task. However, this approach has been shown to lead to unsatisfactory results when the target task is completely unrelated to the data employed for the pre-training process, which is the case when working on document layout analysis. For this reason, in the present paper, we provide an overview of domain-specific transfer learning for document layout segmentation. In particular, we show how relying on document-related images for the pre-training process leads to consistently improved performance and faster convergence compared to training from scratch or even relying on a large, general purpose, dataset such as ImageNet

U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscripts

Document Layout Analysis, which is the task of identifying different semantic regions inside of a document page, is a subject of great interest for both computer scientists and humanities scholars as it represents a fundamental step towards further analysis tasks for the former and a powerful tool to improve and facilitate the study of the documents for the latter. However, many of the works currently present in the literature, especially when it comes to the available datasets, fail to meet the needs of both worlds and, in particular, tend to lean towards the needs and common practices of the computer science side, leading to resources that are not representative of the humanities real needs. For this reason, the present paper introduces U-DIADS-Bib, a novel, pixel-precise, non-overlapping and noiseless document layout analysis dataset developed in close collaboration between specialists in the fields of computer vision and humanities. Furthermore, we propose a novel, computer-aided, segmentation pipeline in order to alleviate the burden represented by the time-consuming process of manual annotation, necessary for the generation of the ground truth segmentation maps. Finally, we present a standardized few-shot version of the dataset (U-DIADS-BibFS), with the aim of encouraging the development of models and solutions able to address this task with as few samples as possible, which would allow for more effective use in a real-world scenario, where collecting a large number of segmentations is not always feasible