Towards a Digital Infrastructure for Illustrated Handwritten Archives

Andreas Weber, Mahya Ameryan, Katherine Wolstencroft, Lise Stork, Maarten Heerlien, Lambert Schomaker

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    18 Citations (Scopus)
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    Large and important parts of cultural heritage are stored in archives that are difficult to access, even after digitization. Documents and notes are written in hard-to-read historical handwriting and are often interspersed with illustrations. Such collections are weakly structured and largely inaccessible to a wider public and scholars. Traditionally, humanities researchers treat text and images separately. This separation extends to traditional handwriting recognition systems. Many of them use a segmentation free OCR approach which only allows the resolution of homogenous manuscripts in terms of layout, style and linguistic content. This is in contrast to our infrastructure which aims to resolve heterogeneous handwritten manuscript pages in which different scripts and images are narrowly intertwined. Authors in our use case, a 17,000 page account of exploration of the Indonesian Archipelago between 1820–1850 (“Natuurkundige Commissie voor Nederlands-Indië”) tried to follow a semantic way to record their knowledge and observations, however, this discipline does not exist in the handwriting script. The use of different languages, such as German, Latin, Dutch, Malay, Greek, and French makes interpretation more challenging. Our infrastructure takes the state-of-the-art word retrieval system MONK as starting point. Owing to its visual approach, MONK can handle the diversity of material we encounter in our use case and many other historical collections: text, drawings and images. By combining text and image recognition, we significantly transcend beyond the state-of-the art, and provide meaningful additions to integrated manuscript recognition. This paper describes the infrastructure and presents early results.

    Keywords: Deep learning · Digital heritage · Natural history
    Biodiversity heritage
    Original languageEnglish
    Title of host publicationDigital Cultural Heritage
    Subtitle of host publicationFinal Conference of the Marie Skłodowska-Curie Initial Training Network for Digital Cultural Heritage, ITN-DCH 2017, Olimje, Slovenia, May 23–25, 2017, Revised Selected Papers
    EditorsMarinos Ioannides
    Place of PublicationCham
    Number of pages12
    ISBN (Electronic)978-3-319-75826-8
    ISBN (Print)978-3-319-75825-1
    Publication statusPublished - Mar 2018

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    • Deep learning (DL)
    • Digital heritage
    • Natural history
    • Biodiversity heritage
    • Digital humanities
    • Emerging technology


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