The cuneiform script constitutes one of the earliest systems of writing and is realized by wedge-shaped marks on clay tablets. A tremendous number of cuneiform tablets have already been discovered and are incrementally digitalized and made available to automated processing. As reading cuneiform script is still a manual task, we address the real-world application of recognizing cuneiform signs by two graph based methods with complementary runtime characteristics. We present a graph model for cuneiform signs together with a tailored distance measure based on the concept of the graph edit distance. We propose efficient heuristics for its computation and demonstrate its effectiveness in classification tasks experimentally. To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size. In order to overcome this issue, we propose to use CNNs adapted to graphs as an alternative approach shifting the computational cost to the training phase. We demonstrate the practicability of both approaches in an extensive experimental comparison regarding runtime and prediction accuracy. Although currently available annotated real-world data is still limited, we obtain a high accuracy using CNNs, in particular, when the training set is enriched by augmented examples.
Automated sign detection across the Electronic Babylonian Library: A large-scale dataset and end-to-end cuneiform OCR pipeline
Institute of Assyriology and Hittite Studies, LMU Munich（慕尼黑大学亚述学与赫梯学研究所）；Department of Statistics, LMU Munich（慕尼黑大学统计系）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Zero-Shot SAM2 Segmentation and Vision Transformer-Based Recognition of Elamite Cuneiform Symbols from Degraded Tablet Images
Vellore Institute of Technology（韦洛尔理工学院）；Indian Institute of Technology（印度理工学院）；WestCliff University（韦斯特克利夫大学）；York St John University（约克圣约翰大学）；Norwegian University of Science and Technology（挪威科技大学）
A Cross-Font Image Retrieval Network for Recognizing Undeciphered Oracle Bone Inscriptions
Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能学院）；School of Informatics, Xiamen University（厦门大学信息学院）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism（文化和旅游部闽南（厦门大学）非物质文化遗产数字化保护与智能处理重点实验室）
Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild
South China University of Technology（华南理工大学）；The University of Adelaide（阿德莱德大学）；Monash University（莫纳什大学）；SCUT-Zhuhai Institute of Modern Industrial Innovation（华南理工大学珠海现代产业创新研究院）
Recognizing and Reconstructing a Multi-Unit Floor Plan
Department of Control and Instrumentation, Brno University of Technology, Brno, Czech Republic（控制与仪器系，布拉格技术大学，布拉格，捷克共和国）；Department of Software Technology, Faculty of Electrical Engineering Mathematics and Computer Science, TU Delft, Delft, Netherlands（软件技术系，电气工程数学与计算机科学学院，代尔夫特理工大学，代尔夫特，荷兰）；Department of Management in the Built Environment, Faculty of Architecture and the Built Environment, TU Delft, Delft, Netherlands（建筑环境管理系，建筑与环境学院，代尔夫特理工大学，代尔夫特，荷兰）；Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Ostrava, Czech Republic and with Department of Informatics, Mendel University in Brno, Brno, Czech Republic（模糊建模研究与应用研究所，奥斯特拉瓦大学，奥斯特拉瓦，捷克共和国，并与布拉格梅德勒大学信息系联合）；Department of Software Technology, Faculty of Electrical Engineering Mathematics and Computer Science, TU Delft, Delft, Netherlands and with Dependable and Scalable Software Systems, Institute of Systems Engineering, Faculty of Electrical Engineering and Computer Science, Leibniz University Hannover, Hannover, Germany（软件技术系，电气工程数学与计算机科学学院，代尔夫特理工大学，代尔夫特，荷兰，并与可靠和可扩展软件系统，系统工程研究所，电气工程与计算机科学学院，莱比锡大学汉诺威分校，汉诺威，德国）
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；ICube（ICube研究中心）；UMR7357（法国大学-研究中心7357）；IHU Strasbourg（斯特拉斯堡IHU医院）；University Digestive Health Care Center – Clarunis（大学消化健康中心 – Clarunis）；Ludwig Maximilian University of Munich（慕尼黑路易斯·马克西米利安大学）；Chinese University of Hong Kong（香港中文大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Creating awareness about security and safety on highways to mitigate wildlife-vehicle collisions by detecting and recognizing wildlife fences using deep learning and drone technology
Rhodes University（罗德斯大学）；University of Ngaoundéré（恩冈代雷大学）
ADAT: Time-Series-Aware Adaptive Transformer Architecture for Sign Language Translation
College of Information Technology, United Arab Emirates University（阿联酋大学信息技术学院）；Emirates Center for Mobility Research, United Arab Emirates University（阿联酋大学阿联酋移动性研究中心）
Sign language recognition from skeletal data using graph and recurrent neural networks
Department of Mathematics, Universidad Autónoma de Ciudad Juárez (UACJ)（数学系，自治城朱埃雷斯大学）；Department of Electrical ingeniering, Universidad Autónoma de Ciudad Juárez (UACJ)（电气工程系，自治城朱埃雷斯大学）；Instituto Iberoamericano San Patricio（伊比利亚美洲圣帕特里克研究所）
