document analysis and recognition

The goal of this workshop is to bridge the gap between the different research fields analyzing handwritten scripts in ancient artifacts. It is primarily targeted at computer scientists, natural scientists, and humanists involved in the study of ancient writing systems and their materials, but it is not limited to these groups. By promoting discussion among these three communities, the workshop aims to encourage future interdisciplinary collaborations that will address current research questions about ancient manuscripts.

The workshop is planned for half a day with a keynote talk, oral presentations, and lively discussions. Submitted papers will be peer-reviewed and accepted submissions will be presented in the workshop and published with ICDAR proceedings in the Springer Lecture Notes in Computer Science (LNCS) series.

Institutions

• Centre for the Study of Manuscript Cultures, ICDAR, IAPR

Computational paleography merges computational methods with the study of ancient scripts, transcending traditional humanities boundaries through technology. It unites experts in manuscripts, computer science, and natural sciences, breaking down chronological and geographical barriers. This interdisciplinary approach allows for detailed analysis of ancient documents, benefiting from advancements in computer vision and analytical techniques. The aim is to facilitate collaboration and discussion among specialists, enhancing understanding and access to cultural heritage, thereby enriching society.
The workshop is planned for half a day with a keynote talk, oral presentations, and lively discussions. Submitted papers will be peer-reviewed and accepted submissions will be presented in the workshop and published with ICDAR proceedings in the Springer Lecture Notes in Computer Science (LNCS) series.

Institution

• Centre for the Study of Manuscript Cultures, ICDAR, IAPR

The study of ancient written artifacts has led to an ever-growing repository of digital data, encompassing raw images of artifacts and automatically generated outputs from advanced acquisition techniques. Manual analysis of this data is not only time-consuming but also prone to human error and bias. Consequently, the integration of novel machine learning and AI methodologies offers significant potential to streamline and enhance the efficiency of artefact research. Computer vision, in particular, provides opportunities beyond handwriting text recognition to address complex challenges in manuscript studies. As a computer scientist, exploring the boundless possibilities of computer vision to resolve research questions in this field has been an enriching experience. This lecture delves into key challenges encountered throughout this journey, highlighting ongoing collaborative projects. These projects illustrate the wide range of diverse research topics, including detecting specific handwriting styles on palm leaves from south India, measuring the density of sieve prints in Himalayan paper-making, addressing the complexities of multilingual texts and varied writing tools in Rilke’s Notebooks from Austria, and even restoring undertexts in Georgian palimpsests. These projects demonstrate the transformative potential of computer vision in advancing manuscript studies.