it - Information Technology最新文献

Discrete Morse theory segmentation on high-resolution 3D lithic artifacts 对高分辨率三维石制品进行离散莫尔斯理论分割

it - Information Technology Pub Date : 2024-06-12 DOI: 10.1515/itit-2023-0027

Jan Philipp Bullenkamp, Theresa Kaiser, F. Linsel, S. Krömker, Hubert Mara

{"title":"Discrete Morse theory segmentation on high-resolution 3D lithic artifacts","authors":"Jan Philipp Bullenkamp, Theresa Kaiser, F. Linsel, S. Krömker, Hubert Mara","doi":"10.1515/itit-2023-0027","DOIUrl":"https://doi.org/10.1515/itit-2023-0027","url":null,"abstract":"Abstract Motivated by the question of understanding the roots of tool making by anatomically modern humans and coexisting Neanderthals in the Paleolithic, a number of shape classification methods have been tested on photographs and drawings of stone tools. Since drawings contain interpretation and photographs fool both human and computational methods by color and shadows on the surface, we propose an approach using 3D datasets as best means for analyzing shape, and rely on first open access repositories on lithic tools. The goal is to not only analyze shape on an artifact level, but allow a more detailed analysis of stone tools on a scar and ridge level. A Morse-Smale complex (MS complex) extracted from the triangular mesh of a 3D model is a reduced skeleton consisting of linked lines on the mesh. Discrete Morse theory makes it possible to obtain such a MS complex from a scalar function. Thus, we begin with Multi-Scale Integral Invariant filtering on the meshes of lithic artifacts, which provides curvature measures for ridges, which are convex, and scars, which are concave. The resulting values on the vertices can be used as our discrete Morse function and the skeleton we get is build up from lines that will coincide with the ridges and, implicitly, contains the scars as enclosed regions of those lines on the mesh. As this requires a few parameters, we provide a graphical user interface (GUI) to allow altering the predefined parameters to quickly find a good result. In addition, a stone tool may have areas that do not belong to the scar/ridge class. These can be masked and we use conforming MS complexes to ensure that the skeleton keeps these areas whole. Finally, results are shown on real and open access datasets. The source code and manually annotated ground truth for the evaluation are provided as Open Access with a Creative Commons license.","PeriodicalId":512610,"journal":{"name":"it - Information Technology","volume":"99 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141352222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Signed, sealed, delivered – digital approaches to Byzantine sigillography 签字、盖章、交付--拜占庭石刻的数字化方法

it - Information Technology Pub Date : 2024-06-07 DOI: 10.1515/itit-2023-0030

Claes Neuefeind, Jan Bigalke, Maria Teresa Catalano, Sviatoslav Drach, Sofia Efthymoglou, Pia Evening, Martina Filosa, Christos Malatras, Marcel Schaeben, Claudia Sode

引用次数: 0

Three degrees of separation: networks in the city of Babylon during the Reign of Darius I (522–486 BCE) 三度分隔：大流士一世统治时期巴比伦城的网络（公元前 522-486 年）

it - Information Technology Pub Date : 2024-06-06 DOI: 10.1515/itit-2023-0062

Jinyan Wang

引用次数: 0

Sign detection for cuneiform tablets 楔形文字碑的标志检测

it - Information Technology Pub Date : 2024-06-03 DOI: 10.1515/itit-2024-0028

Yunus Cobanoglu, Luis Sáenz, Ilya Khait, Enrique Jiménez

{"title":"Sign detection for cuneiform tablets","authors":"Yunus Cobanoglu, Luis Sáenz, Ilya Khait, Enrique Jiménez","doi":"10.1515/itit-2024-0028","DOIUrl":"https://doi.org/10.1515/itit-2024-0028","url":null,"abstract":"\u0000 Among the many excavated cuneiform tablets, only a small portion has been analyzed by Assyriologists. Learning how to read cuneiform is a lengthy and challenging process that can take years to complete. This work aims to improve the automatic detection of cuneiform signs from 2D images of cuneiform tablets. The results can later be used for NLP tasks such as semantic annotation, word alignment and machine translation to assist Assyriologists in their research. We introduce the largest publicly available annotated dataset of cuneiform signs to date. It comprises of 52,102 signs from 315 fully annotated tablets, equating to 512 distinct images. In addition, we have preprocessed and refined four existing datasets, resulting in a comprehensive collection of 88,536 signs. Since some signs are not localized on fully annotated tablets, the total dataset encompasses 593 fully annotated cuneiform tablets, resulting in 654 images. Our efforts to expand this dataset are ongoing. Furthermore, we evaluate two state-of-the-art methods to establish benchmarks in the field. The first is a two-stage supervised sign detection approach that involves: (1) the identification of bounding boxes, and (2) the classification of each sign within these boxes. The second method employs an object detection model. Given the numerous classes and their varied distribution, the task of cuneiform sign detection poses a significant challenge in machine learning. This paper aims to lay a groundwork for future research, offering both a substantial dataset and initial methodologies for sign detection on cuneiform tablets.","PeriodicalId":512610,"journal":{"name":"it - Information Technology","volume":"19 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141228556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Keep me PoS-ted: experimenting with Part-of-Speech prediction on Old Babylonian letters Keep me PoS-ted：在古巴比伦字母上进行语音部分预测实验

it - Information Technology Pub Date : 2024-05-21 DOI: 10.1515/itit-2023-0129

Gustav Ryberg Smidt, Katrien De Graef, Els Lefever

{"title":"Keep me PoS-ted: experimenting with Part-of-Speech prediction on Old Babylonian letters","authors":"Gustav Ryberg Smidt, Katrien De Graef, Els Lefever","doi":"10.1515/itit-2023-0129","DOIUrl":"https://doi.org/10.1515/itit-2023-0129","url":null,"abstract":"\u0000 Within this paper we will account for a cooperation between Ghent University based Assyriologists and computational linguists that has set up a pilot study to analyse the language used in Old Babylonian (OB) letters using Natural Language Processing (NLP) techniques. OB letters make up an interesting dataset because (1) they form an invaluable source for everyday vernacular language, and (2) more than 5000 have been recovered, many of which are accessible in transliteration and translation through the series Altbabylonische Briefe and the Cuneiform Digital Library Initiative. Based on a first batch of letters from OB Sippar, later extended by other Akkadian letters, we aim to develop machine learning approaches to perform semi-automatic text analysis and annotation of the letters. We will here present a Part-of-Speech (PoS) tag prediction model using machine learning. The input data is Akkadian in transliteration and the best performing model is a fine-tuned Multilingual BERT Transformer with Word embeddings (weighted avg F1: 90.19 %). When compared to the benchmark attempt of PoS tagging on a larger Akkadian corpus (97.67 %), it leaves room for improvement. However, analysing the results shows us that multilingual word embeddings improve the model performance and with an enlargement of the corpus targeting certain classes, we could considerably better the macro average F1 scores.","PeriodicalId":512610,"journal":{"name":"it - Information Technology","volume":"29 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141117115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stylistic classification of cuneiform signs using convolutional neural networks 利用卷积神经网络对楔形符号进行文体分类

it - Information Technology Pub Date : 2024-04-08 DOI: 10.1515/itit-2023-0114

Vasiliy Yugay, Kartik Paliwal, Yunus Cobanoglu, Luis Sáenz, Ekaterine Gogokhia, S. Gordin, Enrique Jiménez

引用次数: 0

Preparing multi-layered visualisations of Old Babylonian cuneiform tablets for a machine learning OCR training model towards automated sign recognition 为机器学习 OCR 训练模型准备巴比伦古楔形文字片的多层可视化，以实现自动符号识别

it - Information Technology Pub Date : 2024-01-02 DOI: 10.1515/itit-2023-0063

Hendrik Hameeuw, Katrien De Graef, Gustav Ryberg Smidt, Anne Goddeeris, Timo Homburg, Krishna Kumar Thirukokaranam Chandrasekar

{"title":"Preparing multi-layered visualisations of Old Babylonian cuneiform tablets for a machine learning OCR training model towards automated sign recognition","authors":"Hendrik Hameeuw, Katrien De Graef, Gustav Ryberg Smidt, Anne Goddeeris, Timo Homburg, Krishna Kumar Thirukokaranam Chandrasekar","doi":"10.1515/itit-2023-0063","DOIUrl":"https://doi.org/10.1515/itit-2023-0063","url":null,"abstract":"Abstract In the framework of the CUNE-IIIF-ORM project the aim is to train an Artificial Intelligence Optical Character Recognition (AI-OCR) model that can automatically locate and identify cuneiform signs on photorealistic representations of Old Babylonian texts (c. 2000–1600 B.C.E.). In order to train the model, c. 200 documentary clay tablets have been selected. They are manually annotated by specialist cuneiformists on a set of 12 still raster images generated from interactive Multi-Light Reflectance images. This image set includes visualisations with varying light angles and simplifications based on the dept information on the impressed signs in the surface. In the Cuneur Cuneiform Annotator, a Gitlab-based web application, the identified cuneiform signs are annotated with polygons and enriched with metadata. This methodology builds a qualitative annotated training corpus of approximately 20,000 cropped signs (i.e. 240,000 visualizations), all with their unicode codepoint and conventional sign name. It will act as a multi-layerd core dataset for the further development and fine-tuning of a machine learning OCR training model for the Old Babylonian cuneiform script. This paper discusses how the physical nature of handwritten inscribed Old Babylonian documentary clay tablets challenges the annotation and metadating task, and how these have been addressed within the CUNE-IIIF-ORM project to achieve an effective training corpus to support the training of a machine learning OCR model. ACM CCS Applied computing → Document management and text processing → Document capture → Optical character recognition; Applied computing → Arts and humanities → Language translation.","PeriodicalId":512610,"journal":{"name":"it - Information Technology","volume":"15 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139389955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0