Digitisation in the cultural heritage sector has produced large but fragmented repositories of museum collection data, spanning structured catalogue records, images, and unstructured descriptions. Existing museum information systems often make it difficult to integrate these sources into a unified, queryable representation that supports relation-aware exploration. We present MuseKG, an interactive knowledge graph system that organises heterogeneous museum data into a typed graph that links objects, people, organisations, images, image-derived labels, and extracted semantic entities within a coherent schema. MuseKG supports natural-language queries by grounding user questions to graph entities and retrieving a compact neighbourhood of evidence for answer generation. Through an interactive demonstration on real museum collections, we show that MuseKG supports common exploration tasks such as attribute lookup, relation exploration, and relation-aware retrieval, with answers that remain inspectable via explicit graph structures.

Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically. Recently, there has been a rising demand for developing document understanding among different domains, including business, law, and medicine, to boost the efficiency of work that is associated with a large number of documents. This workshop aims to bring together researchers and industry developers in the field of document intelligence and understanding diverse document types to boost automatic document processing and understanding techniques. We also release a data challenge on the recently introduced document-level VQA dataset, PDFVQA. The PDFVQA challenge examines the model's structural and contextual understandings on the natural full document level of multiple consecutive document pages by including questions with a sequence of answers extracted from multi-pages of the full document. This task helps to boost the document understanding step from the single-page level to the full document level understanding.

This paper presents the Australian sign language (Auslan) fingerspelling recognizer (APR): a system capable of recognizing signs consisting of Auslan manual alphabet letters from video sequences. The APR system uses a combination of geometric features and motion features based on optical flow which are extracted from video sequences. The sequence of features are then classified using hidden Markov models (HMMs). Tests using a vocabulary of twenty signed words showed the system could achieve 97% accuracy at the letter level and 88% at the word level by using a finite state grammar network and embedded training.

Abstract Signed English is a manual interpretation of English using fingerspelling and signs. A prototype of the Hand Sign Translator (HST) system was developed to graphically translate English into Signed English, using two‐handed animation. The HST consists of a practical interface that aims to help users learn Signed English, and the translation process where English text is transformed into a series of images that represent corresponding signs. This paper describes the translation process which involves two stages; the input environment and the animation process. The input environment consists of text analysis in order to extract corresponding kinematic data from the database, named English‐Sign Dictionary (ESD). The data is then used as an input to the animation process, Firstly, the skeleton models of keyframe images and their in‐between poses are calculated. Secondly, appropriate volume models are applied in order to surround the surface of skin. Then the shapes that are suitable for painting are generated, and finally images are drawn and rendered using a smooth animation technique.