Automated Error Detection in Digitized Cultural Heritage Documents

International audienceThe work reported in this paper aims at performance optimization in the digitization of documents pertaining to the cultural heritage domain. A hybrid method is roposed, combining statistical classification algorithms and linguistic knowledge to automatize post-OCR error detection and correction. The current paper deals with the integration of linguistic modules and their impact on error detection

Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach

The linguistic and social impact of multiculturalism can no longer be neglected in any sector, creating the urgent need of creating systems and procedures for managing and sharing cultural heritages in both supranational and multi-literate contexts. In order to achieve this goal, text sensing appears to be one of the most crucial research areas. The long-term objective of the DigitalMaktaba project, born from interdisciplinary collaboration between computer scientists, historians, librarians, engineers and linguists, is to establish procedures for the creation, management and cataloguing of archival heritage in non-Latin alphabets. In this paper, we discuss the currently ongoing design of an innovative workflow and tool in the area of text sensing, for the automatic extraction of knowledge and cataloguing of documents written in non-Latin languages (Arabic, Persian and Azerbaijani). The current prototype leverages different OCR, text processing and information extraction techniques in order to provide both a highly accurate extracted text and rich metadata content (including automatically identified cataloguing metadata), overcoming typical limitations of current state of the art approaches. The initial tests provide promising results. The paper includes a discussion of future steps (e.g., AI-based techniques further leveraging the extracted data/metadata and making the system learn from user feedback) and of the many foreseen advantages of this research, both from a technical and a broader cultural-preservation and sharing point of view

Machine learning for ancient languages: a survey

Ancient languages preserve the cultures and histories of the past. However, their study is fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from deciphering lost languages to restoring damaged inscriptions, to determining the authorship of works of literature. Technological aids have long supported the study of ancient texts, but in recent years advances in artificial intelligence and machine learning have enabled analyses on a scale and in a detail that are reshaping the field of humanities, similarly to how microscopes and telescopes have contributed to the realm of science. This article aims to provide a comprehensive survey of published research using machine learning for the study of ancient texts written in any language, script, and medium, spanning over three and a half millennia of civilizations around the ancient world. To analyze the relevant literature, we introduce a taxonomy of tasks inspired by the steps involved in the study of ancient documents: digitization, restoration, attribution, linguistic analysis, textual criticism, translation, and decipherment. This work offers three major contributions: first, mapping the interdisciplinary field carved out by the synergy between the humanities and machine learning; second, highlighting how active collaboration between specialists from both fields is key to producing impactful and compelling scholarship; third, highlighting promising directions for future work in this field. Thus, this work promotes and supports the continued collaborative impetus between the humanities and machine learning

Non-display uses of copyright works: Google Books and beyond

Copyright @ 2011 The AuthorsWith the advent of mass digitisation projects, such as the Google Book Search, a peculiar shift has occurred in the way that copyright works are dealt with. Contrary to what has so far been the case, works are turned into machine-readable data to be automatically processed for various purposes without the expression of works being displayed to the public. In the Google Book Settlement Agreement, this new kind of uses is referred to as “non-display uses” of digital works. The legitimacy of these uses has not yet been tested by Courts and does not comfortably fit in the current copyright doctrine, plainly because the works are not used as works but as something else, namely as data. Since non-display uses may prove to be a very lucrative market in the near future, with the potential to affect the way people use copyright works, we examine non-display uses under the prism of copyright principles to determine the boundaries of their legitimacy. Through this examination, we provide a categorisation of the activities carried out under the heading of “non-display uses”, we examine their lawfulness under the current copyright doctrine and approach the phenomenon from the spectrum of data protection law as could apply, by analogy, to the use of copyright works as processable data

A prior case study of natural language processing on different domain

In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model

Border crossing and trespassing? : Expanding digital humanities research to developing peripheries with the novel digital technologies

Peer reviewe

Digital Mathematics Libraries: The Good, the Bad, the Ugly

The idea of a World digital mathematics library (DML) has been around since the turn of the 21th century. We feel that it is time to make it a reality, starting in a modest way from successful bricks that have already been built, but with an ambitious goal in mind. After a brief historical overview of publishing mathematics, an estimate of the size and a characterisation of the bulk of documents to be included in the DML, we turn to proposing a model for a Reference Digital Mathematics Library--a network of institutions where the digital documents would be physically archived. This pattern based rather on the bottom-up strategy seems to be more practicable and consistent with the digital nature of the DML. After describing the model we summarise what can and should be done in order to accomplish the vision. The current state of some of the local libraries that could contribute to the global views are described with more details