Handwritten Text Recognition for Historical Documents in the tranScriptorium Project

""© Owner/Author 2014. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM, In Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage (pp. 111-117) http://dx.doi.org/10.1145/2595188.2595193Transcription of historical handwritten documents is a crucial problem for making easier the access to these documents to the general public. Currently, huge amount of historical handwritten documents are being made available by on-line portals worldwide. It is not realistic to obtain the transcription of these documents manually, and therefore automatic techniques has to be used. tranScriptorium is a project that aims at researching on modern Handwritten Text Recognition (HTR) technology for transcribing historical handwritten documents. The HTR technology used in tranScriptorium is based on models that are learnt automatically from examples. This HTR technology has been used on a Dutch collection from 15th century selected for the tranScriptorium project. This paper provides preliminary HTR results on this Dutch collection that are very encouraging, taken into account that minimal resources have been deployed to develop the transcription system.The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 600707 - tranScriptorium and the Spanish MEC under the STraDa (TIN2012-37475-C02-01) research project.Sánchez Peiró, JA.; Bosch Campos, V.; Romero Gómez, V.; Depuydt, K.; De Does, J. (2014). Handwritten Text Recognition for Historical Documents in the tranScriptorium Project. ACM. https://doi.org/10.1145/2595188.2595193

Presentation of the GRIAL research group and its main research lines and projects on March 2016

[EN]Presentation of the GRIAL research group and its main research lines and projects in the Intelligent System Master Degree of University of Salamanca on March 7th, 2016

Modelling:Thinking in Practice; An Introduction

In this introduction of the HSR Supplement “Models and Modelling between Digital and Humanities - A Multidisciplinary Perspective” we refrain from providing a normative definition of ‘model’ and ‘modelling’ and rather attempt at encircling the current state of the art. In the first instance this chapter provides a very brief overview on modelling as intended as a research strategy applied to scientific fields in the 20th-21st centuries. This overview is followed by a short introduction to modelling in digital humanities, focusing on how modelling has developed into a practical strategy and how it has been theorised. The third part of the introduction presents the scope of the project ”Modelling between digital and humanities: Thinking in practice”. The aim of a project workshop held in 2017, of which this volume collects the proceedings, was to present a multitude of modelling practices from various disciplines together with different theoretical frameworks. The fourth part of this introduction offers an overview of each of the papers in this volume. Finally, a fifth section constitutes the first item of the proceedings as it reproduces an adaptation of the dialogue which was performed to introduce the main topics of the workshop and the scope of the project at the event itself. It serves to illustrate the way we organised the workshop and how the exchanges amongst participants were facilitated

Assessing the impact of OCR quality on downstream NLP tasks

A growing volume of heritage data is being digitized and made available as text via optical character recognition (OCR). Scholars and libraries are increasingly using OCR-generated text for retrieval and analysis. However, the process of creating text through OCR introduces varying degrees of error to the text. The impact of these errors on natural language processing (NLP) tasks has only been partially studied. We perform a series of extrinsic assessment tasks — sentence segmentation, named entity recognition, dependency parsing, information retrieval, topic modelling and neural language model fine-tuning — using popular, out-of-the-box tools in order to quantify the impact of OCR quality on these tasks. We find a consistent impact resulting from OCR errors on our downstream tasks with some tasks more irredeemably harmed by OCR errors. Based on these results, we offer some preliminary guidelines for working with text produced through OCR

Ontologies, Data Modeling, and TEI

This paper discusses the relationships between TEI and ontologies from the perspective of computer-based modeling, understood here as a way to establish meaning. The distinctions between creation and use of models as well as between modeling for production and modeling for understanding are presented and compared with other categorizations or models of modeling. One method of establishing meaning in TEI documents is achieved via linking mechanisms between TEI and external ontologies. How such linking can be done and what it may imply for the semantic openness and usability of TEI documents is the practical focus of this article

Designing a Library of Components for Textual Scholarship

Il presente lavoro affronta e descrive temi legati all'applicazione di nuove tecnologie, di metodologie informatiche e di progettazione software volti allo sviluppo di strumenti innovativi per le Digital Humanities (DH), un’area di studio caratterizzata da una forte interdisciplinarità e da una continua evoluzione. In particolare, questo contributo definisce alcuni specifici requisiti relativi al dominio del Literary Computing e al settore del Digital Textual Scholarship. Conseguentemente, il contesto principale di elaborazione tratta documenti scritti in latino, greco e arabo, nonché testi in lingue moderne contenenti temi storici e filologici. L'attività di ricerca si concentra sulla progettazione di una libreria modulare (TSLib) in grado di operare su fonti ad elevato valore culturale, al fine di editarle, elaborarle, confrontarle, analizzarle, visualizzarle e ricercarle. La tesi si articola in cinque capitoli. Il capitolo 1 riassume il contesto del dominio applicativo e fornisce un quadro generale degli obiettivi e dei benefici della ricerca. Il capitolo 2 illustra alcuni importanti lavori e iniziative analoghe, insieme a una breve panoramica dei risultati più significativi ottenuti nel settore delle DH. Il capitolo 3 ripercorre accuratamente e motiva il processo di progettazione messo a punto. Esso inizia con la descrizione dei principi tecnici adottati e mostra come essi vengono applicati al dominio d'interesse. Il capitolo continua definendo i requisiti, l'architettura e il modello del metodo proposto. Sono così evidenziati e discussi gli aspetti concernenti i design patterns e la progettazione delle Application Programming Interfaces (APIs). La parte finale del lavoro (capitolo 4) illustra i risultati ottenuti da concreti progetti di ricerca che, da un lato, hanno contribuito alla progettazione della libreria e, dall'altro, hanno avuto modo di sfruttarne gli sviluppi. Sono stati quindi discussi diversi temi: (a) l'acquisizione e la codifica del testo, (b) l'allineamento e la gestione delle varianti testuali, (c) le annotazioni multilivello. La tesi si conclude con alcune riflessioni e considerazioni indicando anche possibili percorsi d'indagine futuri (capitolo 5)

Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten Documents

Linked Data is used in various fields as a new way of structuring and connecting data. Cultural heritage institutions have been using linked data to improve archival descriptions and facilitate the discovery of information. Most archival records have digital representations of physical artifacts in the form of scanned images that are non-machine-readable. Optical Character Recognition (OCR) recognizes text in images and translates it into machine-encoded text. This paper evaluates the impact of image processing methods and parameter tuning in OCR applied to typewritten cultural heritage documents. The approach uses a multi-objective problem formulation to minimize Levenshtein edit distance and maximize the number of words correctly identified with a non-dominated sorting genetic algorithm (NSGA-II) to tune the methods' parameters. Evaluation results show that parameterization by digital representation typology benefits the performance of image pre-processing algorithms in OCR. Furthermore, our findings suggest that employing image pre-processing algorithms in OCR might be more suitable for typologies where the text recognition task without pre-processing does not produce good results. In particular, Adaptive Thresholding, Bilateral Filter, and Opening are the best-performing algorithms for the theatre plays' covers, letters, and overall dataset, respectively, and should be applied before OCR to improve its performance.Comment: 25 pages, 4 figure

Atti del IX Convegno Annuale dell'Associazione per l'Informatica Umanistica e la Cultura Digitale (AIUCD). La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica

Proceedings of the IX edition of the annual AIUCD conferenc