XDOCS: An Application to Index Historical Documents

Dematerialization and digitalization of historical documents are key elements for their availability, preservation and diffusion. Unfortunately, the conversion from handwritten to digitalized documents presents several technical challenges. The XDOCS project is created with the main goal of making available and extending the usability of historical documents for a great variety of audience, like scholars, institutions and libraries. In this paper the core elements of XDOCS, i.e. page dewarping and word spotting technique, are described and two new applications, i.e. annotation/indexing and search tool, are presented

Illustrations Segmentation in Digitized Documents Using Local Correlation Features

In this paper we propose an approach for Document Layout Analysis based on local correlation features. We identify and extract illustrations in digitized documents by learning the discriminative patterns of textual and pictorial regions. The proposal has been demonstrated to be effective on historical datasets and to outperform the state-of-the-art in presence of challenging documents with a large variety of pictorial elements

Processing of Byzantine Neume Notation in Ancient Historical Manuscripts

This article presents the principal results of the doctoral thesis “Recognition of neume notation in historical documents” by Lasko Laskov (Institute of Mathematics and Informatics at Bulgarian Academy of Sciences), successfully defended before the Specialized Academic Council for Informatics and Mathematical Modelling on 07 June 2010.Byzantine neume notation is a specific form of note script, used by the Orthodox Christian Church since ancient times until nowadays for writing music and musical forms in sacred documents. Such documents are an object of extensive scientific research and naturally with the development of computer and information technologies the need of a software tool which can assist these efforts is needed. In this paper a set of algorithms for processing and analysis of Byzantine neume notation are presented which include document image segmentation, character feature vector extraction, classifier learning and character recognition. The described algorithms are implemented as an integrated scientific software system.* This work has been partly supported by Grant No. DTK 02/54, Bulgarian Science Fund, Ministry of Education, Youth and Science

Miniature illustrations retrieval and innovative interaction for digital illuminated manuscripts

In this paper we propose a multimedia solution for the interactive exploration of illuminated manuscripts. We leveraged on the joint exploitation of content-based image retrieval and relevance feedback to provide an effective mechanism to navigate through the manuscript and add custom knowledge in the form of tags. The similarity retrieval between miniature illustrations is based on covariance descriptors, integrating color, spatial and gradient information. The proposed relevance feedback technique, namely Query Remapping Feature Space Warping, accounts for the user’s opinions by accordingly warping the data points. This is obtained by means of a remapping strategy (from the Riemannian space where covariance matrices lie, referring back to Euclidean space) useful to boost the retrieval performance. Experiments are reported to show the quality of the proposal. Moreover, the complete prototype with user interaction, as already showcased at museums and exhibitions, is presented

Historical Handwritten Text Images Word Spotting through Sliding Window HOG Features

In this paper we present an innovative technique to semi-automatically index handwritten word images. The proposed method is based on HOG descriptors and exploits Dynamic Time Warping technique to compare feature vectors elaborated from single handwritten words. Our strategy is applied to a new challenging dataset extracted from Italian civil registries of the XIX century. Experimental results, compared with some previously developed word spotting strategies, confirmed that our method outperforms competitors

Offline Recognition of Malayalam and Kannada Handwritten Documents Using Deep Learning

For a variety of reasons, handwritten text can be digitalized. It is used in a variety of government entities, including banks, post offices, and archaeological departments. Handwriting recognition, on the other hand, is a difficult task as everyone has a different writing style. There are essentially two methods for handwritten recognition: a holistic and an analytic approach. The previous methods of handwriting recognition are time- consuming. However, as deep neural networks have progressed, the approach has become more straightforward than previous methods. Furthermore, the bulk of existing solutions are limited to a single language. To recognise multilanguage handwritten manuscripts offline, this work employs an analytic approach. It describes how to convert Malayalam and Kannada handwritten manuscripts into editable text. Lines are separated from the input document first. After that, word segmentation is performed. Finally, each word is broken down into individual characters. An artificial neural network is utilised for feature extraction and classification. After that, the result is converted to a word document

The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition

NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern RecognitionVolume 46, Issue 6, June 2013, Pages 1658–1669 DOI: 10.1016/j.patcog.2012.11.024[EN] Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies. © 2012 Elsevier Ltd. All rights reserved.Work supported by the EC (FEDER/FSE) and the Spanish MEC/MICINN under the MIPRCV ‘‘Consolider Ingenio 2010’’ program (CSD2007-00018), MITTRAL (TIN2009-14633-C03-01) and KEDIHC ((TIN2009-14633-C03-03) projects. This work has been partially supported by the European Research Council Advanced Grant (ERC-2010-AdG-20100407: 269796-5CofM) and the European seventh framework project (FP7-PEOPLE-2008-IAPP: 230653-ADAO). Also supported by the Generalitat Valenciana under grant Prometeo/2009/014 and FPU AP2007-02867, and by the Universitat Politecnica de Val encia (PAID-05-11). We would also like to thank the Center for Demographic Studies (UAB) and the Cathedral of Barcelona.Romero Gómez, V.; Fornés, A.; Serrano Martínez-Santos, N.; Sánchez Peiró, JA.; Toselli ., AH.; Frinken, V.; Vidal, E.... (2013). The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. Pattern Recognition. 46(6):1658-1669. https://doi.org/10.1016/j.patcog.2012.11.024S1658166946

Annotating digital libraries and electronic editions in a collaborative and semantic perspective

The distinction between digital libraries and electronic editions is becom-ing more and more subtle. The practice of annotation represents a point of conver-gence of two only apparently separated worlds. The aim of this paper is to present a model of collaborative semantic annotation of texts (SemLib project), suggesting a system that find in Semantic Web and Linked Data the solution technologies for en-abling structured semantic annotation, also in the field of electronic editions in Digi-tal Humanities domain. The main purpose of SemLib is to develop an application so to make easy for developers the integration of annotation software in digital librar-ies, which are different both for technical implementations and managed contents, and provide to users, indifferently from their cultural backgrounds, a simple system which could be used as a front-end. We present, for this purpose, a final example of semantic annotation in a specific context: a digital edition of a literary text and the issues that an annotation task involves