Search

PurposeHistorical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.Design/methodology/approachIn this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.FindingsThe results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.Originality/valueThe proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).

This demonstrator focuses on one of the end results of the FAME project [1]. The main focus of this project is the development of a generic open-source face recognition pipeline that can be applied on a broad variety of cultural heritage video/image archives, the performing arts collection of the Flanders Arts Institute [2] being one of them. Performing arts images/videos are among the most challenging types of video for face recognition given their dynamic and heterogeneous nature and the variety of domains they span. If it will work on this type of content, it will most probably also work on other types of archived content. Due to the lack of accurate shot-level metadata, the searchability of performing art videos is still rather limited. Mostly they can only be queried based on some global metadata. Retrieving the exact shot(s) where an actor or dancer appears in the video, for example, is currently impossible. The shots can of course be manually annotated, but this is an error-prone and labour-intensive process, definitely when it needs to be performed on a large collection. Within the FAME project, we address this issue and proposes a video content annotation tool for the automatic annotation of faces in performing art videos. The resulting JSON metadata (which contains the Wikidata IDs of the recognized people and the timestamps when they appear in the video) is used in a video browser web application which allows to query the video based on the recognized performers and find the shots they appear in.

The digitization of historical documents has led to a surge in digital and cultural heritage research, highlighting the need for automated tools to process and enrich these collections. Annotating them with rich metadata will improve their accessibility and open new research opportunities. This work details a computer vision-based annotation pipeline, with limited manual annotation to facilitate studies of the Italian economy at the beginning of the last century.

Historically, herbarium specimens have provided users with documented occurrences of plants in specific locations over time. Herbarium collections have therefore been the basis of systematic botany for centuries (Younis et al. 2020). According to the latest summary report based on the data from Index Herbariorum, there are around 3400 active herbaria in the world containing 397 million specimens that are spread across 182 countries (Thiers 2021). Exponential growth in high quality image capturing devices induced by the enormous amount of uncovered collections has further led to rising interest in large scale digitization initiatives across the world (Le Bras et al. 2017). As herbarium specimens are increasingly becoming digitised and accessible in online repositories, an important need has also emerged to develop automated tools to process and enrich these collections to facilitate better access to the preserved archives. This rising number of digitised herbarium sheets provides an opportunity to employ computer-based image processing techniques, such as deep learning, to automatically identify species and higher taxa (Carranza-Rojas and Joly 2018, Carranza-Rojas et al. 2017, Younis et al. 2020) or to extract other useful information from the herbaria sheets, such as detecting handwritten text, color bars, scales and barcodes. The species identification task works well for herbarium sheets that have only one species in a page. However, there are many herbarium books that have multiple species on the same page (as shown in Fig. 1) for which the complexity of the identification problem increases tremendously. It also involves a great deal of time and effort if they are to be enriched manually. In this work, we propose a pipeline that can automatically detect, identify, and enrich plant species in multi-specimen herbaria. The core idea of the pipeline is to detect unique plant species and handwritten text around the plant species and map the text to the correct plant species. As shown in Fig. 2, the proposed pipeline begins with the pre-processing of the images. The images are rotated and aligned such that the longest edge is maintained as its height. In the case of herbarium books, the pages are detected and morphological transformations are performed to reduce occlusions (Thirukokaranam Chandrasekar and Verstockt 2020). A YOLOv3 (You Only Look Once version 3) object detection model (Zhao and Li 2020) is trained from scratch to detect plants and text. The model was trained on a dataset of single species herbarium sheets with a mosaic augmentation technique to extend the plants model to detect multiple species. The first results of the training shows impressive results although it could be further improved with more labelled data. We also plan to train an object segmentation model and contrast its performance with the plant detection model for multi-specimen herbarium sheets. After detecting both the plants and the text, the text will be recognized with a state-of-the-art handwritten text recognition (HTR) model. The recognized text can then be matched with a database of specimens, to identify each detected specimen. Furthermore, additional textual metadata (e.g. date, locality, collector's name, institution) visible on the sheet will be recognized and used to enrich the collection.