The study of ancient documents provides a glimpse into our past. However, the low image quality and intricate details commonly found in these documents present significant challenges for accurate object detection. The objective of this research is to enhance object detection in ancient documents by reducing false positives and improving precision. To achieve this, we propose a method that involves the creation of synthetic datasets through computational mediation, along with the integration of visual feature extraction into the object detection process. Our approach includes associating objects with their component parts and introducing a visual feature map to enable the model to discern between different symbols and document elements. Through our experiments, we demonstrate that improved object detection has a profound impact on the field of Paleography, enabling in-depth analysis and fostering a greater understanding of these valuable historical artifacts.
University of Sfax（斯法克斯大学）；Université de La Rochelle（拉罗谢尔大学）
The analysis of historical documents is still a topical issue given the importance of information that can be extracted and also the importance given by the institutions to preserve their heritage. The main idea in order to characterize the content of the images of ancient documents after attempting to clean the image is segmented blocks texts from the same image and tries to find similar blocks in either the same image or the entire image database. Most approaches of offline handwriting recognition proceed by segmenting words into smaller pieces (usually characters) which are recognized separ…
University of Sfax（斯法克斯大学）；University of La Rochelle（拉罗谢尔大学）
This paper deals with the recognition and matching of text in both cartographic maps and ancient documents. The purpose of this work is to find similar text regions based on statistical and global features. A phase of normalization is done first, in object to well categorize the same quantity of information. A phase of wordspotting is done next by combining local and global features. We make different experiments by combining the different techniques of extracting features in order to obtain better results in recognition phase. We applied fontspotting on both ancient documents and cartographic…
National Technical University of Athens（雅典国家技术大学）；Technical Institute of Chalkida（哈尔基达技术学院）；Ionian University（爱奥尼亚大学）；Furman University（弗曼大学）
In this paper, a novel approach is introduced for classifying curves into proper families, according to their similarity. First, a mathematical quantity we call plane curvature is introduced and a number of propositions are stated and proved. Proper similarity measures of two curves are introduced and a subsequent statistical analysis is applied. First, the efficiency of the curve fitting process has been tested on 2 shapes datasets of reference. Next, the methodology has been applied to the very important problem of classifying 23 Byzantine codices and 46 Ancient inscriptions to their writers…
L3i - EA 2118（L3i实验室（EA 2118））；LaBRI - CNRS 5800（波尔多计算机研究所（CNRS 5800））
In recent years, machine learning and deep learning approaches such as artificial neural networks have gained in popularity for the resolution of automatic puzzle resolution problems. Indeed, these methods are able to extract high-level representations from images, and then can be trained to separate matching image pieces from non-matching ones. These applications have many similarities to the problem of ancient document reconstruction from partially recovered fragments. In this work we present a solution based on a Graph Neural Network, using pairwise patch information to assign labels to edg…
Pontifical Catholic University of Parana (PUCPR)（巴拉那天主教大学）；State University of Ponta Grossa (UEPG)（蓬塔格罗萨州立大学）；École de Technologie Supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Normandie Univ（诺曼底大学）；UNIROUEN（鲁昂大学）；UNIHAVRE（勒阿弗尔大学）；INSA Rouen（鲁昂国立应用科学学院）；Federal University of Paraná（巴拉那联邦大学）
This paper describes two approaches for content-based image retrieval and pattern spotting in document images using deep learning. The first approach uses a pre-trained CNN model to cope with the lack of training data, which is fine-tuned to achieve a compact yet discriminant representation of queries and image candidates. The second approach uses a Siamese Convolution Neural Network trained on a previously prepared subset of image pairs from the ImageNet dataset to provide the similarity-based feature maps. In both methods, the learned representation scheme considers feature maps of different…
Historical Document Image Binarization is a well-known segmentation problem in image processing. Despite ubiquity, traditional thresholding algorithms achieved limited success on severely degraded document images. With the advent of deep learning, several segmentation models were proposed that made significant progress in the field but were limited by the unavailability of large training datasets. To mitigate this problem, we have proposed a novel two-stage framework -- the first of which comprises a generator that generates degraded samples using variational inference and the second being a C…
University of Sfax（斯法克斯大学）；Université de La Rochelle（拉罗谢尔大学）
Characterizing noisy or ancient documents is a challenging problem up to now. Many techniques have been done in order to effectuate feature extraction and image indexation for such documents. Global approaches are in general less robust and exact than local approaches. That's why, we propose in this paper, a hybrid system based on global approach(fractal dimension), and a local one based on SIFT descriptor. The Scale Invariant Feature Transform seems to do well with our application since it's rotation invariant and relatively robust to changing illumination.In the first step the calculation of…
Shenzhen University（深圳大学）；Wuhan University（武汉大学）；Dunhuang Research Academia（敦煌研究院）；University of South Carolina（南卡罗来纳大学）
Cultural heritage is the asset of all the peoples of the world. The preservation and inheritance of cultural heritage is conducive to the progress of human civilization. In northwestern China, there is a world heritage site -- Mogao Grottoes -- that has a plenty of mural paintings showing the historical cultures of ancient China. To study these historical cultures, one critical procedure is to date the mural paintings, i.e., determining the era when they were created. Until now, most mural paintings at Mogao Grottoes have been dated by directly referring to the mural texts or historical docume…
SynDoc: A Hybrid Discriminative-Generative Framework for Enhancing Synthetic Domain-Adaptive Document Key Information Extraction
The University of Western Australia（西澳大学）；The University of Melbourne（墨尔本大学）；The University of Sydney（悉尼大学）；Weill Cornell Medicine, Cornell University（韦尔·科恩医学中心，康奈尔大学）
Doc-CoB: Enhancing Document Understanding with Visual Chain-of-Boxes Reasoning
Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University（浙江可感知与智能系统重点实验室，浙江大学）；Alibaba Group（阿里巴巴集团）；Shanghai AI Laboratory（上海人工智能实验室）
Imprinto: Enhancing Infrared Inkjet Watermarking for Human and Machine Perception
DFKI & Saarland University（DFKI与萨尔兰大学）；MIT CSAIL（MIT计算机科学与人工智能实验室）；Virginia Tech（弗吉尼亚理工大学）；Universidad Carlos III de Madrid（马德里第三大学）；Adobe Research（Adobe研究）
