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J. Imaging
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Recent Advances in Historical Document Processing
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Recent Advances in Historical Document Processing

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 31027

Special Issue Editors

Prof. Dr. Angelo Marcelli

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy
Interests: neurocomputational models of handwriting learning and execution; handwriting analysis and recognition; neural networks and evolutionary computation; historical document processing
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Parziale

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering and Applied Mathematics (DIEM), Università di Salerno, 84084 Salerno, Italy
Interests: movement analysis; handwriting analysis; automatic signature and writer verification; artificial intelligence; pattern recognition; neurodegenerative disorders; computational neuroscience; systems neuroscience; neurocomputational models; neuromusculoskeletal models; neurorobotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Historical documents are the largest repository of our cultural heritage, and their preservation as well as the access to their content by both scholars and the general public have been topics of increasing research and development efforts in the recent past.

This Special Issue welcomes recent advances for automatic processing of historical documents, including image acquisition, restoration, indexing, and retrieval, to encompass the entire processing procedure from image acquisition to information extraction.

List of topics:

Imaging and Image Processing

  • Imaging for fragile materials;
  • Multispectral imaging;
  • Camera-based/non-invasive acquisition;
  • Restoration of image content;
  • Artifacts filtering/removal;
  • Artificial intelligence/machine learning techniques for image enhancement;
  • Interactive tools for image enhancement.

Document Content Extraction and Processing

  • Layout analysis and segmentation;
  • Automated or semi-automated transcription/processing;
  • Keyword Spotting
  • Text recognition
  • Artificial intelligence/machine learning techniques for content extraction;
  • Ontologies for and semantic analysis of historical document content.

Applications

  • Style identification for printed and handwritten documents;
  • Style recognition for manuscript dating or author verification/identification;
  • Large scale historical collection: storing, compressing, searching, indexing, and retrieving issues.
  • Performance evaluation: the user perspective;
  • Case reports in deploying historical document processing systems and tools.

Prof. Dr. Angelo Marcelli
Dr. Antonio Parziale
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

16 pages, 2168 KiB  
Article
A Model for Evaluating the Performance of a Multiple Keywords Spotting System for the Transcription of Historical Handwritten Documents
by Angelo Marcelli, Giuseppe De Gregorio and Adolfo Santoro
J. Imaging 2020, 6(11), 117; https://doi.org/10.3390/jimaging6110117 - 3 Nov 2020
Cited by 2 | Viewed by 1974
Abstract
This paper proposes a performance model for estimating the user time needed to transcribe small collections of handwritten documents using a keyword spotting system (KWS) that provides a number of possible transcriptions for each word image. The model assumes that only information obtained [...] Read more.
This paper proposes a performance model for estimating the user time needed to transcribe small collections of handwritten documents using a keyword spotting system (KWS) that provides a number of possible transcriptions for each word image. The model assumes that only information obtained from a small training set is available, and establishes the constraints on the performance measures to achieve a reduction of the time for transcribing the content with respect to the time required by human experts. The model is complemented with a procedure for computing the parameters of the model and eventually estimating the improvement of the time to achieve a complete and error-free transcription of the documents. Full article
(This article belongs to the Special Issue Recent Advances in Historical Document Processing)
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30 pages, 2104 KiB  
Article
Deep Learning for Historical Document Analysis and Recognition—A Survey
by Francesco Lombardi and Simone Marinai
J. Imaging 2020, 6(10), 110; https://doi.org/10.3390/jimaging6100110 - 16 Oct 2020
Cited by 59 | Viewed by 13381
Abstract
Nowadays, deep learning methods are employed in a broad range of research fields. The analysis and recognition of historical documents, as we survey in this work, is not an exception. Our study analyzes the papers published in the last few years on this [...] Read more.
Nowadays, deep learning methods are employed in a broad range of research fields. The analysis and recognition of historical documents, as we survey in this work, is not an exception. Our study analyzes the papers published in the last few years on this topic from different perspectives: we first provide a pragmatic definition of historical documents from the point of view of the research in the area, then we look at the various sub-tasks addressed in this research. Guided by these tasks, we go through the different input-output relations that are expected from the used deep learning approaches and therefore we accordingly describe the most used models. We also discuss research datasets published in the field and their applications. This analysis shows that the latest research is a leap forward since it is not the simple use of recently proposed algorithms to previous problems, but novel tasks and novel applications of state of the art methods are now considered. Rather than just providing a conclusive picture of the current research in the topic we lastly suggest some potential future trends that can represent a stimulus for innovative research directions. Full article
(This article belongs to the Special Issue Recent Advances in Historical Document Processing)
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17 pages, 1012 KiB  
Article
One Step Is Not Enough: A Multi-Step Procedure for Building the Training Set of a Query by String Keyword Spotting System to Assist the Transcription of Historical Document
by Antonio Parziale, Giuliana Capriolo and Angelo Marcelli
J. Imaging 2020, 6(10), 109; https://doi.org/10.3390/jimaging6100109 - 13 Oct 2020
Cited by 4 | Viewed by 3165
Abstract
Digital libraries offer access to a large number of handwritten historical documents. These documents are available as raw images and therefore their content is not searchable. A fully manual transcription is time-consuming and expensive while a fully automatic transcription is cheaper but not [...] Read more.
Digital libraries offer access to a large number of handwritten historical documents. These documents are available as raw images and therefore their content is not searchable. A fully manual transcription is time-consuming and expensive while a fully automatic transcription is cheaper but not comparable in terms of accuracy. The performance of automatic transcription systems is strictly related to the composition of the training set. We propose a multi-step procedure that exploits a Keyword Spotting system and human validation for building up a training set in a time shorter than the one required by a fully manual procedure. The multi-step procedure was tested on a data set made up of 50 pages extracted from the Bentham collection. The palaeographer that transcribed the data set with the multi-step procedure instead of the fully manual procedure had a time gain of 52.54%. Moreover, a small size training set that allowed the keyword spotting system to show a precision value greater than the recall value was built with the multi-step procedure in a time equal to 35.25% of the time required for annotating the whole data set. Full article
(This article belongs to the Special Issue Recent Advances in Historical Document Processing)
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15 pages, 1057 KiB  
Article
An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents
by Nicole Dalia Cilia, Claudio De Stefano, Francesco Fontanella, Claudio Marrocco, Mario Molinara and Alessandra Scotto di Freca
J. Imaging 2020, 6(9), 89; https://doi.org/10.3390/jimaging6090089 - 4 Sep 2020
Cited by 10 | Viewed by 3844
Abstract
In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decision-making support systems. The quality of the [...] Read more.
In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decision-making support systems. The quality of the results provided by such applications, however, is strongly influenced by the selection of effective features, which should be able to capture the distinctive aspects to which the paleography expert is interested in. This process is very difficult to generalize due to the enormous variability in the type of ancient documents, produced in different historical periods with different languages and styles. The effect is that it is very difficult to define standard techniques that are general enough to be effectively used in any case, and this is the reason why ad-hoc systems, generally designed according to paleographers’ suggestions, have been designed for the analysis of ancient manuscripts. In recent years, there has been a growing scientific interest in the use of techniques based on deep learning (DL) for the automatic processing of ancient documents. This interest is not only due to their capability of designing high-performance pattern recognition systems, but also to their ability of automatically extracting features from raw data, without using any a priori knowledge. Moving from these considerations, the aim of this study is to verify if DL-based approaches may actually represent a general methodology for automatically designing machine learning systems for palaeography applications. To this purpose, we compared the performance of a DL-based approach with that of a “classical” machine learning one, in a particularly unfavorable case for DL, namely that of highly standardized schools. The rationale of this choice is to compare the obtainable results even when context information is present and discriminating: this information is ignored by DL approaches, while it is used by machine learning methods, making the comparison more significant. The experimental results refer to the use of a large sets of digital images extracted from an entire 12th-century Bibles, the “Avila Bible”. This manuscript, produced by several scribes who worked in different periods and in different places, represents a severe test bed to evaluate the efficiency of scribe identification systems. Full article
(This article belongs to the Special Issue Recent Advances in Historical Document Processing)
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20 pages, 13246 KiB  
Article
Cross-Depicted Historical Motif Categorization and Retrieval with Deep Learning
by Vinaychandran Pondenkandath, Michele Alberti, Nicole Eichenberger, Rolf Ingold and Marcus Liwicki
J. Imaging 2020, 6(7), 71; https://doi.org/10.3390/jimaging6070071 - 15 Jul 2020
Cited by 2 | Viewed by 3409
Abstract
In this paper, we tackle the problem of categorizing and identifying cross-depicted historical motifs using recent deep learning techniques, with aim of developing a content-based image retrieval system. As cross-depiction, we understand the problem that the same object can be represented (depicted) in [...] Read more.
In this paper, we tackle the problem of categorizing and identifying cross-depicted historical motifs using recent deep learning techniques, with aim of developing a content-based image retrieval system. As cross-depiction, we understand the problem that the same object can be represented (depicted) in various ways. The objects of interest in this research are watermarks, which are crucial for dating manuscripts. For watermarks, cross-depiction arises due to two reasons: (i) there are many similar representations of the same motif, and (ii) there are several ways of capturing the watermarks, i.e., as the watermarks are not visible on a scan or photograph, the watermarks are typically retrieved via hand tracing, rubbing, or special photographic techniques. This leads to different representations of the same (or similar) objects, making it hard for pattern recognition methods to recognize the watermarks. While this is a simple problem for human experts, computer vision techniques have problems generalizing from the various depiction possibilities. In this paper, we present a study where we use deep neural networks for categorization of watermarks with varying levels of detail. The macro-averaged F1-score on an imbalanced 12 category classification task is 88.3 %, the multi-labelling performance (Jaccard Index) on a 622 label task is 79.5 %. To analyze the usefulness of an image-based system for assisting humanities scholars in cataloguing manuscripts, we also measure the performance of similarity matching on expert-crafted test sets of varying sizes (50 and 1000 watermark samples). A significant outcome is that all relevant results belonging to the same super-class are found by our system (Mean Average Precision of 100%), despite the cross-depicted nature of the motifs. This result has not been achieved in the literature so far. Full article
(This article belongs to the Special Issue Recent Advances in Historical Document Processing)
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17 pages, 10112 KiB  
Article
CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation
by Yekta Said Can and M. Erdem Kabadayı
J. Imaging 2020, 6(5), 32; https://doi.org/10.3390/jimaging6050032 - 14 May 2020
Cited by 12 | Viewed by 4271
Abstract
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) [...] Read more.
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. Full article
(This article belongs to the Special Issue Recent Advances in Historical Document Processing)
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