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Fabric of a town is an accumulation of historical scenes. Historic towns are regarded as a treasured inheritance. Royal towns in Malaysia is significant to unique heritage identity. Streets in royal town are significant urban elements that indicates the characteristics and attributes of a setting that define their physical and functional aspects. The aim of this study is to analyze the physical components and qualities that influenced the street aspect of the royal town in Kuala Kangsar. It discusses the background of the town, literature review on traditional street characteristics and analyzing physical elements as attributes in revealing physical qualities that ultimately form a unique and distinctive street character in the royal town. The research employed mixed methods, of combining qualitative and quantitative analysis to strengthen the conclusions and heightened its knowledge and validity. This paper will share its findings based on research’s pilot study through questionnaire, observation, interviews and historical document analysis. The royal heritage town was analyzed to illustrate the objectives and all attributes were cross-investigated to determine what are the essential physical elements that have contributed to these qualities. The study concludes that attractiveness, visibility, human scale, enclosure, permeability, legibility and special activities are the significant physical qualities that shaped the character of streets in the royal town. Each physical quality is manifested by the physical elements that becomes the catalyst in creating a successful path.

Binarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

Having Brazilian teaching as the object of research, this article seeks to answer the following question from a historical perspective: which Brazilian teaching practices are described in the pedagogical literature of the second half of the 20th century? Documentary analysis of three works of great impact in the educational literature is carried out and a brief analytical exercise is proposed. Far from exhausting these documentary sources, this analysis seeks to find some clues about the impact of these concepts on teaching in basic education that predominated in Brazil. Thus, the three trends that comprise (and continue to comprise) the guidelines of academic research in this period are mapped: in the 1960s, the articulations between teaching and traditionalism; in the 1980s, discussions about technical competence and political commitment; and, in the 1990s, centrality in teachers’ practice. Al tener la docencia brasileña como objeto de investigación, desde una perspectiva histórica, el presente artículo intenta contestar la siguiente interrogante: ¿qué prácticas es posible describir acerca de la docencia brasileña en la literatura pedagógica de la segunda mitad del siglo XX? Se realiza un análisis documental de tres obras de importante impacto en la literatura educacional y se propone un breve ejercicio analítico que, en lugar de agotar tales fuentes documentales, trata de mapear algunas pistas acerca de las concepciones de investigación sobre la docencia en la educación básica que predominaron en Brasil. De este modo, se mapean tres tendencias que formaron parte (y todavía forman) de las pautas de la investigación académica en tal periodo, es decir: en la década de 1960, las articulaciones entre docencia y tradicionalismo; en la década de 1980, los debates en torno a la competencia técnica y el compromiso político; y en los años 1990, la centralidad en la práctica de los profesores. Cet article, dont l’objet de recherche est l’enseignement brésilien, cherche à répondre dans une perspective historique à la question suivante: quelles pratiques d’enseignement sont décrites dans les ouvrages pédagogiques de la seconde moitié du XXe siècle au Brésil? L’étude effectue d’abord une analyse de trois oeuvres d’impact sur le champ educationnel et propose un bref exercice analytique. Loin d’épuiser les sources documentaires, il vise à tracer quelques pistes concernant les conceptions de la recherche en enseignement qui ont prédominé dans l’éducation brésilienne. Trois tendances qui composaient, et composent toujours, les lignes directrices de la recherche de la période sont degagées: les articulations entre enseignement et traditionalisme pour les années 1960; les débats autour de la compétence technique et de l’engagement politique; dans les années 1980 et dans les années 1990, la centralité de la pratique des enseignants. Tendo a docência brasileira como objeto de pesquisa, a partir de uma perspectiva histórica, o presente artigo procura responder ao seguinte questionamento: quais práticas podem ser descritas acerca da docência brasileira na literatura pedagógica da segunda metade do século XX? Realiza-se uma análise documental de três obras de importante impacto na literatura educacional e propõe-se um breve exercício analítico, que, longe de esgotar tais fontes documentais, busca mapear algumas pistas acerca das concepções de pesquisa sobre a docência na educação básica que predominaram no Brasil. Assim, são mapeadas três tendências que compuseram (e ainda compõem) as pautas da pesquisa acadêmica nesse período, quais sejam: na década de 1960, as articulações entre docência e tradicionalismo; na década de 1980, os debates em torno da competência técnica e do compromisso político; e nos anos 1990, a centralidade na prática dos professores.

Over the past few decades, the use of scanned historical document images has increased dramatically, especially with the emergence of online libraries and standard benchmark datasets like DIBCO. The historical documents are usually in very-poor conditions containing noises like large ink stains, bleed-through, liquid spills, uneven-background, spots, faded-ink, weak/thin text that makes the task of binarization very difficult. In this paper, we propose an effective degraded document image binarization algorithm that performs accurate text segmentation. Our method first estimates the background utilizing information from neighboring pixels and filter smoothening. The next step is background subtraction that helps in the compensation of background distortions. The document is segmented using Otsu thresholding, and then we process the image to remove the remaining noise and maximize text content using labelled connected components. Our method outperforms several existing and widely used binarization algorithms on F-measure, PSNR, DRD, and pseudo F-measure when evaluated on H-DIBCO 2016 and H-DIBCO 2018 datasets and can very effectively detect faint characters from a document image.

RESUMO Este artigo é um estudo histórico e historiográfico a partir de objeto museológico, um colar que é exemplar do Ferro de Berlim, tipo europeu em voga na primeira metade do século XIX, e de sua ficha catalográfica. O colar, cuja ficha catalográfica atribui a construção à Real Fábrica de Ferro São João do Ipanema, de São Paulo, foi, supostamente, presente dedicado à Dona Teresa Cristina. O objeto e sua ficha catalográfica são indícios para a reflexão e o levantamento de hipóteses sobre a doação feita pela memória civilizatória brasileira. Ao longo da pesquisa por referências para a proposição de uma biografia do objeto, surgiram incógnitas e outras possibilidades foram identificadas para a peça, que reiteram a potência da cultura material como documento histórico. ABSTRACT This is a historical and historiographical study based on a museum object and its catalogue card (object description and information about it). The necklace - a Berlin Iron exemplar, a fashionable European product in the first half of the 19th century - and its museum catalogue card, which attributes its construction to the Royal Iron Factory São João do Ipanema, from São Paulo, and supposedly a gift dedicated to the Empress Teresa Cristina, are indications for hypotheses and reflection on the donation made as a confirmation of the Brazilian civilizing memory. Throughout the search for references for proposing a biography of the object, uncertainties emerged and other possibilities were identified for the piece, which reiterate the huge potential of the material culture as a historical document.

The purpose of the article is to clarify the specific features of documentary information resources determine the theoretical foundations of the process of their classification, development of classification models based on documentary and source methodology. Research methodology. Methods of historical document science, source studies, archival science were applied, which allowed establishing the attribute of the documentary information resource and its classification within the limits of document science theory. The scientific novelty of the work is to establish the specific features of documentary information resources, identify information characteristics, disclose internal and external features of documents, their further classification as a documentary source of information stored in archives, museums, and libraries. Conclusions. The study made it possible to generalize the theoretical knowledge of historical records management and source study about the specific features of the documentary-information resource and the process of their subsequent classification. Systematization and classification of documentary and information resources expand the possibilities of studying the theoretical foundations of a document, allows you to create a holistic comprehensive vision of the external and internal features of a document as an information resource and a means of communication from the point of view of theoretical and thematic-applied problems. Historical records management, archival studies, and source studies provide for the use of interdisciplinary methodological foundations of scientific research of documentary and information resources. The process of classifying documentary and informational resources of a specific problem-historical study includes the fact that they are an organic component of the informational past of Ukraine.Key words: classification, documentary information resource, document, historical document science, source science, archival science, social communication.

Introduction This is a comprehensive history of the International Society for Sexual Medicine (ISSM) and its founding organizations regarding the publications, including the journals and the publication committee, and the communication tools of the organization since its inception. Objectives The object of this review is to provide a detailed and comprehensive history of the publication and communication tools of the ISSM and the people who have participated in production of these efforts. Methods Recorded Publication Committee minutes, filed letters, the various journals themselves, printed News bulletins, and Publishers reports to the society served as source documents to produce this history. The author has participated in many of the journal establishments and has kept an extensive personnel file of the events related in this history. All written history has not only relied on personal memories of these events but have been verified from the stored personal files. Printed and website stored journal and News bulletin have served as source material for this history. Also, Power Point presentations by the editors of the journals at the Publication Committee meetings have served as source material. Finally, annual, and semi-annual reports of the Publishers presented at Publication Committee meetings of the ISSM are source material. Results After extensive review of the historical material listed in the Methods section of this abstract, this comprehensive history of the communication efforts of this society has provided a rich and dynamic historical document for this society. Conclusion This extensive, detailed, and comprehensive history of the communication tools of this society help us to record and remember the events and the people involved in this process. Sharing scientific information and information regarding the life of the International Society for Sexual Medicine have been an important function of this society from early. Lewis RW. Comprehensive History of the International Society for Sexual Medicine–Journals and Communication. Sex Med Rev 2021;9:542–567.

Binarization is a key step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of the encoder-and-decoder architecture, such as fully convolutional network (FCN) and U-Net. Despite their success, they still suffer from two challenges: (1) max-pooling or strided convolution reduces the spatial resolution of the intermediate feature maps, which may lead to information loss, and (2) interpolation or transposed convolution attempts to restore the feature maps to the desired spatial resolution, which may also result in pixelation. To overcome these two limitations, we propose a fully dilated convolutional network, termed FD-Net, using atrous convolutions instead of downsampling or upsampling operations. We have conducted extensive experiments on the recent DIBCO (document image binarization competition) and H-DIBCO (handwritten document image binarization competition) benchmark datasets. The experimental results show that our proposed FD-Net outperforms other state-of-the-art techniques by a large margin. The source code and pre-trained models are publicly available at https://github.com/beargolden/FD-Net.

V dnešní době dosáhlo rozpoznávání pojmenovaných entit (NER) vynikajících výsledků na standardních korpusech. Objevují se však velké problémy při jejich použití ve specifické doméně, protože rozpoznávání vyžaduje vhodný anotovaný korpus. To je patrné zejména v oblasti zpracování historických dokumentů. Hlavním cílem tohoto příspěvku je navrhnout a srovnat několik metod přenosu učení (transfer learning) ke zvýšení skóre českého historického NER. Studujeme několik informačních zdrojů a pro rozpoznávání používáme dvě neuronové sítě. Pro vyhodnocení metod využíváme dva korpusy: českých pojmenovaných entit a českých historických pojmenovaných entit. Ukazujeme, že BERT reprezentace s doladěním a jednoduchým klasifikátorem natrénovaným na spojených korpusech dosahuje vynikajících výsledků. Nowadays, named entity recognition (NER) achieved excellent results on the standard corpora. However, big issues are emerging with a need for an application in a specific domain, because it requires a suitable annotated corpus with adapted NE tag-set. This is particularly evident in the historical document processing field. The main goal of this paper consists of proposing and evaluation of several transfer learning methods to increase the score of the Czech historical NER. We study several information sources, and we use two neural nets for NE modeling and recognition. We employ two corpora for evaluation of our transfer learning methods, namely Czech named entity corpus and Czech historical named entity corpus. We show that BERT representation with fine-tuning and only the simple classifier trained on the union of corpora achieves excellent results.

The presence of struck-out texts in handwritten manuscripts adversely affects the performance of state-of-the-art automatic handwritten document processing systems. The information of struck-out words (STW) are often important for real-time applications like handwritten character recognition, writer identification, digital transcription, forensic applications, historical document analysis etc. Hence, the detection of STW and localisation of struck-out strokes (SS) are crucial tasks. In this paper, we introduce a system for simultaneous detection of STWs and localisation of the SS using a single network architecture based on Generative Adversarial Network (GAN). The system requires no prior information about the type of SS stroke and it is also able to robustly handle variant of strokes like straight, slanted, cris-cross, multiple-lines, underlines and partial STW as well. However, we also present a methodology to generate STW with high variability of SS for network learning. We have evaluated the proposed pipeline on publicly available IAM dataset and also on struck-out words collected from real-world writers with high variability factors like age, gender, stroke-width, stroke-type etc. The evaluation metrics show robustness and applicability in real-world scenario.

The primary objective of this paper is to examine the concept of the pluralism in Islam in accordance with the Qur’anic perspective. What does the Qur’an say about the pluralistic society? Does the Qur’an accept the concept of pluralism? Is there any room for Non-Muslims and atheists in the Muslim society? How does the Qur’an treat any non-Muslim community? It is generally believed that Islam promotes an exclusive society of its own. Yet, the prophet Muhammed lived in a pluralistic society. He accepted the concept of pluralism in his lifetime. The covenant of Medina is a historical document that justifies the concept of pluralism in Islam. This paper examines all these questions in this short article.

International audience; This competition investigated the performance of historical document classification. The analysis of historical documents is a difficult challenge commonly solved by trained humanists. We provided three different classification tasks, which can be solved individually or jointly: font group/script type, location, date. The document images are provided by several institutions and are taken from handwritten and printed books as well as from charters. In contrast to previous competitions, all participants relied upon Deep Learning based approaches. Nevertheless, we saw a great performance variety of the different submitted systems. The easiest task seemed to be font group recognition while the script type classification and location classification were more challenging. In the dating task, the best system achieved a mean absolute error of about 22 years.

The segmentation of complex images into semantic regions has seen a growing interest these last years with the advent of Deep Learning. Until recently, most existing methods for Historical Document Analysis focused on the visual appearance of documents, ignoring the rich information that textual content can offer. However, the segmentation of complex documents into semantic regions is sometimes impossible relying only on visual features and recent models embed both visual and textual information. In this paper, we focus on the use of both visual and textual information for segmenting historical registers into structured and meaningful units such as acts. An act is a text recording containing valuable knowledge such as demographic information (baptism, marriage or death) or royal decisions (donation or pardon). We propose a simple pipeline to enrich document images with the position of text lines containing key-phrases and show that running a standard image-based layout analysis system on these images can lead to significant gains. Our experiments show that the detection of acts increases from 38 % of mAP to 74 % when adding textual information, in real use-case conditions where text lines positions and content are extracted with an automatic recognition system.

This address was held by me on 28 December 1918 in the Great Hall of the Philharmonie in Berlin in front of an uncommonly numerous mixed audience. The organiser was one Association for Civic and Economic Education [Arbeitsgemeinschaft fur Staatsburgerliche und Wirtschaftliche Bildung], which in the first, febrile days of the German Republic took an interest in widely elucidating its tasks and possibilities. At its instigation, the address was recorded stenographically, and distributed as a pamphlet with quite a high circulation. This pamphlet, which was regrettably printed based on the uncorrected stenograph, has already been out of print for some time. But since again and again enquiries have been made after it, I have now carefully looked through it, and so I am once again putting it out onto the book market, divided into chapters to give a better overview. My perusal only concerned its style. Factually I have left it unchanged, since I am conscious that it is a piece from the first, hopeful months of the Revolution, which on account of the tone that pervades it might, in its own way, also be regarded as a historical document.

This paper presents an end-to-end attention-based neural network for identifying writers in historical documents. The proposed network does not require any preprocessing stages such as binarization or segmentation. It has three main parts: a feature extractor using Convolutional Neural Network (CNN) to extract features from an input image; an attention filter to select key points; and a generalized deep neural VLAD model to form a representative vector by aggregating the extracted key points. The whole network is trained end-to-end by a combination of cross-entropy and triplet losses. In the experiments, we evaluate the performance of our model on the HisFragIR20 dataset that consists of about 120,000 historical fragments from many writers. The experiments demonstrate better mean average precision and accuracy at top-1 in comparison with the state-of-the-art results on the HisFragIR20 dataset. This model is rather new for dealing with various sizes of historical document fragments in the writer identification and image retrieval.

Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers). In a real-life scenario, obtaining sample images for all possible words is also not feasible. However, Zero-shot learning methods could aptly be used to recognize unseen/out-of-lexicon words in such historical document images. Based on previous state-of-the-art methods for word spotting and recognition, we propose a hybrid representation that considers the character’s shape appearance to differentiate between two different words and has shown to be more effective in recognizing unseen words. This representation has been termed as Pyramidal Histogram of Shapes (PHOS), derived from PHOC, which embeds information about the occurrence and position of characters in the word. Later, the two representations are combined and experiments were conducted to examine the effectiveness of an embedding that has properties of both PHOS and PHOC. Encouraging results were obtained on two publicly available historical document datasets and one synthetic handwritten dataset, which justifies the efficacy of “Phos” and the combined “Pho(SC)” representation.

Labeling the layout in historical documents for preparing training data for machine learning techniques is an arduous task that requires great human effort. A draft of the layout can be obtained by using a document layout analysis (DLA) system that later can be corrected by the user with less effort than doing it from scratch. We research in this paper an iterative process in which the user only supervises and corrects the given draft for the pages automatically selected by the DLA system with the aim of reducing the required human effort. The results obtained show that similar DLA quality can be achieved by reducing the number of pages that the user has to annote and that the accumulated human effort required to obtain the layout of the pages used to train the models can be reduced more than \(95\%\).

Image denoising is one of the most important steps in the document image analysis pipeline thanks to its good effect into the rest of the workflow. However, the noise in historical documents is totally different from the common noise present in other classical problems of image processing. It is particularly the case of the image of Cham inscriptions obtained by the stamping of ancient stele. In this paper, we leverage the advantage of deep learning to adapt with these noisy conditions. The proposed network follows an encoder-decoder structure by combining convolution/deconvolution operators with symmetrical skip connections and residual blocks for improving reconstructed image. Furthermore, global attention fusion is proposed to learn the relevant regions in the image. Our experiments demonstrate the proposed method can’t only remove unwanted parts in the image, but also enhance the visual quality for the Cham inscriptions.

El entorno de la Educación Física escolar es uno de los campos de estudio menos tratados para abordar los antecedentes y orígenes del fútbol en España. Partiendo de esta premisa, el objeto de estudio de este artículo es el de adéntranos a una revisión histórica sobre esta cuestión. La introducción del fútbol en las clases de Gimnástica del Instituto de segunda enseñanza de Palma de Mallorca por iniciativa del profesor Adolfo Fernández Revuelta ofrece la posibilidad de extrapolar la situación a otros institutos y colegios del país. Un estudio histórico de base documental y la interpretación constructivista de los hechos conducen a una exposición que se muestra concluyente para precisar que el entorno escolar de los colegios e institutos de segunda enseñanza fue decisivo para que el fútbol emergiera con éxito entre la juventud española de principios del siglo XX. School physical education is one of the fields of study that have received the least attention when dealing with the historical background and origins of football in Spain. The inclusion of football as part of Gymnastics lessons at the Palma de Mallorca secondary school, on the initiative of the teacher Adolfo Fernández Revuelta, provides an opportunity to extrapolate this situation to other schools and colleges in the country. A historical document-based study and a constructivist interpretation of events lead to the firm conclusion that secondary schooling was a decisive factor in the emerging popularity of football among Spanish youth at the start of the 20th century School physical education is one of the fields of study that have received the least attention when dealing with the historical background and origins of football in Spain. On this consideration, the object of study of this article is to initiate a historical review on this question. The inclusion of football as part of Gymnastics lessons at the Palma de Mallorca secondary school, on the initiative of the teacher Adolfo Fernández Revuelta, provides an opportunity to extrapolate this situation to other schools and colleges in the country. A historical document-based study and a constructivist interpretation of events lead to the firm conclusion that secondary schooling was a decisive factor in the emerging popularity of football among Spanish youth at the start of the 20th century

Through the analysis of literature and literature communication mode, the construction and use of database under the background of big data, the meaning of data intelligence and the construction of historical literature model, this paper expounds that big data and the data intelligence developed on the basis of it have a great promotion effect on the historical research work. Using literature research method, concept analysis method, comparative research method and so on, this paper probes into the way to construct the historical document model under the data intelligent visual fault. Data intelligence emergence enables a historical document to break away from its own single individual, to break the time and space constraints, to connect with similar or similar documents, and then to integrate multiple and dispersed textual information. In particular, data intelligence, through the ability of data acquisition and calculation, can actively screen and compare abstract information in heterogeneous data according to internal logic, can reconstruct the historical original appearance, and can make it be a widely used knowledge, and then can provide research hypothesis and prediction model for research work. This artificial intelligence technology which highlights the subjectivity of literature is marked by deep learning, and further deepens the study of big data.

The information provided by historical documents has always been indispensable in the transmission of human civilization, but it has also made these books susceptible to damage due to various factors. Thanks to recent technology, the automatic digitization of these documents are one of the quickest and most effective means of preservation. The main steps of automatic text digitization can be divided into two stages, mainly: character segmentation and character recognition, where the recognition results depend largely on the accuracy of segmentation. Therefore, in this study, we will only focus on the character segmentation of historical Chinese documents. In this research, we propose a model named HRCenterNet, which is combined with an anchorless object detection method and parallelized architecture. The MTHv2 dataset consists of over 3000 Chinese historical document images and over 1 million individual Chinese characters; with these enormous data, the segmentation capability of our model achieves IoU 0.81 on average with the best speed-accuracy trade-off compared to the others. Our source code is available at https://github.com/Tverous/HRCenterNet.

With the increasing number of digitization efforts of historical manuscripts and archives, automatical information retrieval systems need to extract meaning fast and reliably. Historical archives bring more challenges for these systems when compared to modern manuscripts. More advanced algorithms, archive specific methods, preprocessing techniques are needed to retrieve information. Cutting-edge machine learning algorithms should also be applied to retrieve meaning from these documents. One of the most important research issues of historical document analysis is the lack of public datasets. Although there are plenty of public datasets for modern document analysis, the number of public annotated historical archives is limited. Researchers can test novel algorithms on these modern datasets and infer some results, but their performance is unknown without testing them on historical datasets. In this study, we created a historical Arabic handwritten digit dataset by combining manual annotation and automatic document analysis techniques. The dataset is open for researchers and contained more than 6000 digits. We then tested deep transfer learning algorithms and various machine learning techniques to recognize these digits and achieved promising results.

Character recognition of historical document is one of the most important basic tasks in the digitization of historical document. This paper is aimed at the few-shot learning problem of Korean ancient character recognition, a data enhancement method that combines traditional data and a conditional deep convolution generation confrontation network is proposed to obtain expanded samples. Then analyzed the performance of the Lenet@5 and Lenet@8 network models in Korean ancient character recognition. The experimental results show that the expanded sample significantly enriches the experimental data, and the improved Lenet@8 network model is better than Lenet@5, which can better acquire image features and greatly improve the classification accuracy. The proposed method can solve the problem of Recognition of Text-Image Characters of Korean Historical Document.

The present work focuses on combining Convolutional Neural Network (CNN) features to strengthen the writer retrieval in historical document databases. Various CNN models that are LeNet, ResNet, and VGG are used to get writer-independent features of handwritten documents. These features are associated with dissimilarity measures to achieve the retrieval task. Then, developed writer retrieval systems are combined through SVM classifier. Experiments are conducted on ICDAR-2017 dataset which contains historical handwritten documents. The results obtained highlight the robustness of CNN features and the combination stage as well.

This competition succeeds upon a line of competitions for writer and style analysis of historical document images. In particular, we investigate the performance of large-scale retrieval of historical document fragments in terms of style and writer identification. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists. In comparison to previous competitions, we make the results more meaningful by addressing the issue of sample granularity and moving from writer to page fragment retrieval. The two approaches, style and author identification, provide information on what kind of information each method makes better use of and indirectly contribute to the interpretability of the participating method. Therefore, we created a large dataset consisting of more than 120 000 fragments. Although the most teams submitted methods based on convolutional neural networks, the winning entry achieves an mAP below 40%., ICFHR 2020

The purpose of this article is to comprehend both the objectification and the formalization of the historical document named Alboroto y Motin de Mexico, therefore, it is intended to emphasize the narrative focus of Don Carlos de Siguenza y Gongora. Thus, the discursive nuances in the epistle of this erudite of New Spain are clarified, making a critique of the historiography with ethical overtones.

The Proceedings of the Old Bailey is a corpus of over 180,000 page images of court records printed from April 1674 to April 1913 and presents a comprehensive challenge for Optical Character Recognition (OCR) services. The Old Bailey is an ideal benchmark for historical document OCR, representing more than two centuries of variations in documents, including spellings, formats, and printing and preservation qualities. In addition to its historical and sociological significance, the Old Bailey is filled with imperfections that reflect the reality of coping with large-scale historical data. Most importantly, the Old Bailey contains human transcriptions for each page, which can be used to help measure OCR accuracy. Since humans do make mistakes in transcriptions, the relative performance of OCR services will be more informative than their absolute performance. This paper compares three leading commercial OCR cloud services: Amazon Web Services's Textract (AWS); Microsoft Azure's Cognitive Services (Azure); and Google Cloud Platform's Vision (GCP). Benchmarking involved downloading over 180,000 images, executing the OCR, and measuring the error rate of the OCR text against the human transcriptions. Our results found that AWS had the lowest median error rate, Azure had the lowest median round trip time, and GCP had the best combination of a low error rate and a low duration.

International audience; This paper introduces a new corpus of multilin-gual medieval handwritten charter images, annotated with fulltranscription and named entities. The corpus is used to com-pare two approaches for named entity recognition in historicaldocument images in several languages: on the one hand, asequential approach, more commonly used, that sequentiallyapplies handwritten text recognition (HTR) and named entityrecognition (NER), on the other hand, a combined approachthat simultaneously transcribes the image text line and extractsthe entities. Experiments conducted on the charter corpus inLatin, early new high German and old Czech for name, dateand location recognition demonstrate a superior performance ofthe combined approach.

International audience; State-of-the-art Document Layout Analysis methodsrely on graphical appearance features in order to detect andclassify the different layout regions present in a scanned textimage. In many cases, however, performing this task using onlygraphical information is problematic or impossible. Only byactually reading some text in the boundaries of the problematicregions it becomes possible to reliably detect and separate theseregions. In these situations, textual, content-based features wouldbe required, but since transcription is usually performed afterlayout analysis, a vicious circle arises. In this work, we circumventthis deadlock by making use of the recently introduced concept ofProbabilistic Index Map. We use the word relevance probabilitiesprovided by this map to calculate relevant text content basedfeatures at the pixel level. We assess the impact of these newfeatures on a historical document complex paragraph classifica-tion task. The experiments are performed using both a classicalHidden Markov Model approach and Deep Neural Networks.The obtained results are encouraging and showcase the positiveimpact text content based features will have on the DocumentLayout Analysis research field.

Museum collections not only provide educational tools for the public, but also reference material for osteological research and baseline information for understanding historical population dynamics and food webs. Such applications are only possible, however, with accurate identifications of museum osteological specimens, which is sometimes challenging, as specimens can be separated from their original information. In order to clarify missing information about provenience and species identifications, we analysed 13 historical whale bone specimens from the Museum of Zoology, Strasbourg, using a multidisciplinary approach that combined historical document analysis, osteology, and Zooarchaeology by Mass Spectrometry (ZooMS). These analyses enabled identification of elements from seven different whale species: five baleen whales (Mysticeti) and two toothed whales (Odontoceti). Two skeletons could be connected with letters from the early 1900s indicating they derived from whales stranded on the shores of South Island, New Zealand. One of these skeletons was digitized using a 3D scanner and can be freely viewed online. All of the samples will be available through a digital archive. The identification and digitization of these museum whale specimens, which include threatened and endangered whale species whose past histories are not yet fully understood, is of great value and ensures they are fully available for future systematic research. The study demonstrates a new and useful application of ZooMS, particularly in concert with other methods, to support museum collections research. 1. Introduction 2. Material and Methods 2.1. Assessment of collection 2.2. Historical documents 2.3. Osteological analysis 2.4. Peptide mass fingerprinting (ZooMS) 2.5. Overall identifications 3. Results 3.1. Description of the bones 3.2. Documents and historical data 3.3. Osteological determinations and observations 3.4. ZooMS identifications 3.5. Combining identifications and reuniting individuals 4. Discussion 5. Conclusion

The paper presents Intergraph, a graph-based visual analytics technical demonstrator for the exploration and study of content in historical document collections. The designed prototype is motivated by a practical use case on a corpus of circa 15.000 digitized resources about European integration since 1945. The corpus allowed generating a dynamic multilayer network which represents different kinds of named entities appearing and co-appearing in the collections. To our knowledge, Intergraph is one of the first interactive tools to visualize dynamic multilayer graphs for collections of digitized historical sources. Graph visualization and interaction methods have been designed based on user requirements for content exploration by non-technical users without a strong background in network science, and to compensate for common flaws with the annotation of named entities. Users work with self-selected subsets of the overall data by interacting with a scene of small graphs which can be added, altered and compared. This allows an interest-driven navigation in the corpus and the discovery of the interconnections of its entities across time.

The history of neuroscience is the memory of the discipline and this memory depends on the study of the present traces of the past; the things left behind: artifacts, equipment, written documents, data books, photographs, memoirs, etc. History, in all of its definitions, is an integral part of neuroscience and I have used examples from the literature and my personal experience to illustrate the importance of the different aspects of history in neuroscience. Each time we talk about the brain, do an experiment, or write a research article, we are involved in history. Each published experiment becomes a historical document; it relies on past research (the "Introduction" section), procedures developed in the past ("Methods" section) and as soon as new data are published, they become history and become embedded into the history of the discipline ("Discussion" section). In order to be transparent and able to be replicated, each experiment requires its own historical archive. Studying history means researching books, documents and objects in libraries, archives, and museums. It means looking at data books, letters and memos, talking to scientists, and reading biographies and autobiographies. History can be made relevant by integrating historical documents into classes and by using historical websites. Finally, conducting historical research can be interesting, entertaining, and can lead to travel to out-of-the-way and exotic places and meeting interesting people.

In this paper, we propose a historical document image denoising method based on Ising-like model and Anisotropic filter. Physical statistics and morphological smooth process are combined together to improve image particulars. We first apply an Ising model to the 8-th bit plane of the original historical document image, then the image energy field and density is through Ising dynamic evolution. After the image is processed, it has been smoothed by Anisotropic morphological for several times. At last, we get the restored image document. We compare the visual quality changes before and after Ising evolution in different Anisotropic morphological condition respectively. Experimental results demonstrate that the Ising evolved image can better restore the local area details of the degraded historical document image.

In this paper, we propose an end-to-end trainable framework for restoring historical documents content that follows the correct reading order. In this framework, two branches named character branch and layout branch are added behind the feature extraction network. The character branch localizes individual characters in a document image and recognizes them simultaneously. Then we adopt a post-processing method to group them into text lines. The layout branch based on fully convolutional network outputs a binary mask. We then use Hough transform for line detection on the binary mask and combine character results with the layout information to restore document content. These two branches can be trained in parallel and are easy to train. Furthermore, we propose a re-score mechanism to minimize recognition error. Experiment results on the extended Chinese historical document MTHv2 dataset demonstrate the effectiveness of the proposed framework., 6 pages, 6 figures

This article uses the study of two environmental, social, and governance (ESG) data vendors—KLD and Innovest—to exemplify the “social origins of ESG issues” argument made by Eccles and Stroehle in their 2018 working paper “Exploring Social Origins in the Construction of ESG Measures.” Based on in-depth interviews with the organizations’ founders and historical document analysis, we recap the history of the cases and show how different origins, philosophies, and “purposes” of ESG issues shaped the methods and data characteristics of two of the most important data vendors of their time. We discuss why MSCI chose to continue with the financial value–oriented methodology of Innovest while discontinuing the values-driven KLD methodology. Through an in-depth literature analysis, we further show that not only the creation but also the use of “nonfinancial performance” concepts rely on processes of social construction. We also show that investors use different ESG data from those used by academics, potentially leading to misaligned narratives. Finally, with this article we join the call for more explicit contextualization of ESG data, highlighting that both practitioners and academics need to better understand the social construction that underlies analyses that use different concepts of ESG.

The purpose of the article is to analyze and characterize the Roman military diploma as a historical document. The research methodology consists in the use of general scientific methods and principles: a comparative and historiographical analysis, systematization of scientific literature, research works and documents; the principle of historicism, systematicity, scientificity and objectivity. The scientific novelty: on the basis of a wide range of the research es for the first time in the Ukrainian historiography the significance of the Roman military diploma as a historical document ha s been characterized; the prospects of the study of diplomata militum as the sources for the study of the Roman influences on the territory of ancient Ukraine have been elucidated . The Conclusions . During m any years the research es in the field of epigraph ics , including the study of military diplomas, became an important component in the reconstruction of the personnel of the Roman auxiliary units – auxilia, the names of these units, and helped reproduce some other aspects of functioning related not only to the military history. This refers to the administrative apparatus of the Empire, in particular, in addition to the then ruling emperor, in the diplomas the names of consuls, the governor of the province and the commanders of these military units are recorded ; you can also trace the stages of their career – cursus honorum. Analyzing these documents, we can determine the status and place of retired servicemen in their residence places after the honorary resignation ( honesta missio ), to trace the stages and nature of the Romanization of the provinces owing to the quantity of diplomas found and the information about the retiree’s family. It ha s been also noted that the Roman military diplomas from the territory of Ukraine can be an important source for studying some aspects of the history of ancient Ukraine. Nowadays, many issues related to the Roman military diplomas remain not analyzed. For instance, the place of publishing of the imperial constitutions (tabula aenae (aerae)), from which the diplomas were copied , has not yet been identified. The place the diplomas were stored has not been identified either. It is unknown why bronze documents were stopped issuing in the ІІІd century and what are the reasons for their appearance on the territory of the Ukrainian lands. Despite all these gaps, many aspects of this issue have already been studied and this gives us the opportunity to look in a new way at the functioning of such a complex part of the Roman state mechanism as the Roman army.

Kanuni Sultan Süleyman’ın tahta çıktıktan sonraki ikinci büyük seferi Rodos Seferi’dir. İstanbul’dan Marmaris’e kadarki sefer güzergâhı üzerinde Padişah ve ordu birçok önemli yerleşim yerleri, kasaba, cami, medrese, han, hamam, menzil, geçit, nehir ve köprüden geçmiştir. Bunlardan bazıları daha önce ve sonrası ordunun sefer güzergâhları üzerinde yer almış ve özellikle Nakkaş Matrakçı Nasuh’un fırçasından bize aktarılmışlardır. Nakkaşların kaynak eserlerine de yer verilmiştir. Seferle ilgili en önemli kaynak, yazma ruzname günlükleri, ruznamenin yer vermediği bazı konular için başvurulan kaynak, daha sonra tarihli Evliya Çelebi’nin yazdığı seyahatname olmuştur. Konular, minyatürün teknikleri, üslup özellikleri ve belgeleme özellikleri göz önünde bulundurularak tasarlanmıştır. Sefer süresinde Osmanlı Devleti’nin yaklaşık yedi ay yönetildiği Muğla Sancağı ve çevresi konulu daha önce tarihi, topograf özellikli minyatürler yapılmamıştır. Muğla Sancağı ile ilgili örnek minyatürler ilk örnekleri teşkil etmektedir. Makale de yer verilen minyatürler tarihi belge özelliklerinin yanında günümüz minyatür sanatı ile ileriki dönemlere kaynak olması; nakkaşları, sanatçıları ve araştırmacıları ele alınmamış yeni konulara ele almaya yönlendirmesi umulmaktadır. The second major expedition of Suleyman the Magnificent after he ascended the throne was the Rhodes Campaign. On the expedition route from İstanbul to Marmaris, the sultan and the army passed through many important settlements, towns, mosques, madrasahs, inns, baths, ranges, rives, passages and bridges. Some of these have been on the expedition routes of the army before and after and they have been transferred to us, especially from the brush of the knocker Matrakçı Nasuh. The most important resource about the expedition was the historical diary, and the source used for some issues that were not included in the diary was the travel book written by Evliya Çelebi. The topics are organized taking into account the techniques, style and documentation features of the miniature. Historical, topographic miniatures in the Sanjak of Muğla and its surroundings, where the Ottoman Empire was ruled for seven months during the expedition, have not been studied before. In addition to the historical document features, the miniatures included in the article are expected to be a source for the next periods and today’s miniature art, directing the artists and researchers to new topics that have not been addressed before.

Historical document analysis systems gain importance with the increasing efforts in the digitalization of archives. Page segmentation and layout analysis are crucial steps for such systems. Errors in these steps will affect the outcome of handwritten text recognition and Optical Character Recognition (OCR) methods, which increase the importance of the page segmentation and layout analysis. Degradation of documents, digitization errors, and varying layout styles are the issues that complicate the segmentation of historical documents. The properties of Arabic scripts such as connected letters, ligatures, diacritics, and different writing styles make it even more challenging to process Arabic script historical documents. In this study, we developed an automatic system for counting registered individuals and assigning them to populated places by using a CNN-based architecture. To evaluate the performance of our system, we created a labeled dataset of registers obtained from the first wave of population registers of the Ottoman Empire held between the 1840s and 1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places., European Union (European Union); Horizon 2020; European Research Council (ERC); Research and Innovation Program Grant; UrbanOccupationsOETR

This review provides a comprehensive view of the field of historical document image binarization with a focus on the contributions made in the last decade. After the introduction of a standard benchmark dataset with the 2009 Document Image Binarization Contest, research in the field accelerated. Besides the standard methods for image thresholding, preprocessing, and post-processing, we review the literature on methods such as statistical models, pixel classification with learning algorithms, and parameter tuning. In addition to reviewing binarization algorithms, we discuss the available public datasets and evaluation metrics, including those that require pixel-level ground truth and those that do not. We conclude with recommendations for future work.

This chapter gives an overview of the state of the art and recent methods in the area of historical document analysis. Historical documents differ from the ordinary documents due to the presence of ...

Logo and Seal serves the purpose of authenticating and referring to the source of a document. This strategy was also prevalent in the medieval period. Different algorithm exists for detection of logo and seal in document images. A close look into the present state-of-the-art methods reveals that those methods were focused toward detection of logo and seal in contemporary document images. However, such methods are likely to underperform while dealing with historical documents. This is due to the fact that historical documents are attributed with additional challenges like extra noise, bleed-through effect, blurred foreground elements and low contrast. The proposed method frames the problem of the logo and seals detection in an object detection framework. Using a deep-learning technique it counters earlier mentioned problems and evades the need for any pre-processing stage like layout analysis and/or binarization in the system pipeline. The experiments were conducted on historical images from 12th to the 16th century and the results obtained were very encouraging for detecting logo in historical document images. To the best of our knowledge, this is the first attempt on logo detection in historical document images using an object-detection based approach.

Image binarization is one of the most relevant preprocessing operations influencing the results of further image analysis conducted for many purposes. During this step a significant loss of information occurs and the use of inappropriate thresholding methods may cause difficulties in further shape analysis or even make it impossible to recognize different shapes of objects or characters. Some of the most typical applications utilizing the analysis of binary images are Optical Character Recognition (OCR) and Optical Mark Recognition (OMR), which may also be applied for unevenly illuminated natural images, as well as for challenging degraded historical document images, considered as typical benchmarking tools for image binarization algorithms.