12,995 research outputs found

    Transcribing a 17th-century botanical manuscript: Longitudinal evaluation of document layout detection and interactive transcription

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    [EN] We present a process for cost-effective transcription of cursive handwritten text images that has been tested on a 1,000-page 17th-century book about botanical species. The process comprised two main tasks, namely: (1) preprocessing: page layout analysis, text line detection, and extraction; and (2) transcription of the extracted text line images. Both tasks were carried out with semiautomatic pro- cedures, aimed at incrementally minimizing user correction effort, by means of computer-assisted line detection and interactive handwritten text recognition technologies. The contribution derived from this work is three-fold. First, we provide a detailed human-supervised transcription of a relatively large historical handwritten book, ready to be searchable, indexable, and accessible to cultural heritage scholars as well as the general public. Second, we have conducted the first longitudinal study to date on interactive handwriting text recognition, for which we provide a very comprehensive user assessment of the real-world per- formance of the technologies involved in this work. Third, as a result of this process, we have produced a detailed transcription and document layout infor- mation (i.e. high-quality labeled data) ready to be used by researchers working on automated technologies for document analysis and recognition.This work is supported by the European Commission through the EU projects HIMANIS (JPICH program, Spanish, grant Ref. PCIN-2015-068) and READ (Horizon-2020 program, grant Ref. 674943); and the Universitat Politecnica de Valencia (grant number SP20130189). This work was also part of the Valorization and I+D+i Resources program of VLC/CAMPUS and has been funded by the Spanish MECD as part of the International Excellence Campus program.Toselli, AH.; Leiva, LA.; Bordes-Cabrera, I.; Hernández-Tornero, C.; Bosch Campos, V.; Vidal, E. (2018). Transcribing a 17th-century botanical manuscript: Longitudinal evaluation of document layout detection and interactive transcription. Digital Scholarship in the Humanities. 33(1):173-202. https://doi.org/10.1093/llc/fqw064S173202331Bazzi, I., Schwartz, R., & Makhoul, J. (1999). An omnifont open-vocabulary OCR system for English and Arabic. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(6), 495-504. doi:10.1109/34.771314Causer, T., Tonra, J., & Wallace, V. (2012). Transcription maximized; expense minimized? Crowdsourcing and editing The Collected Works of Jeremy Bentham*. Literary and Linguistic Computing, 27(2), 119-137. doi:10.1093/llc/fqs004Ramel, J. Y., Leriche, S., Demonet, M. L., & Busson, S. (2007). User-driven page layout analysis of historical printed books. International Journal of Document Analysis and Recognition (IJDAR), 9(2-4), 243-261. doi:10.1007/s10032-007-0040-6Romero, V., Fornés, A., Serrano, N., Sánchez, J. A., Toselli, A. H., Frinken, V., … Lladós, J. (2013). The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. Pattern Recognition, 46(6), 1658-1669. doi:10.1016/j.patcog.2012.11.024Romero, V., Toselli, A. H., & Vidal, E. (2012). Multimodal Interactive Handwritten Text Transcription. Series in Machine Perception and Artificial Intelligence. doi:10.1142/8394Toselli, A. H., Romero, V., Pastor, M., & Vidal, E. (2010). Multimodal interactive transcription of text images. Pattern Recognition, 43(5), 1814-1825. doi:10.1016/j.patcog.2009.11.019Toselli, A. H., Vidal, E., Romero, V., & Frinken, V. (2016). HMM word graph based keyword spotting in handwritten document images. Information Sciences, 370-371, 497-518. doi:10.1016/j.ins.2016.07.063Bunke, H., Bengio, S., & Vinciarelli, A. (2004). Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 709-720. doi:10.1109/tpami.2004.1

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

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    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

    Document segmentation using Relative Location Features

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    [ES] Presentamos un método genérico para análisis de layout ideado para trabajar sobre documentos con layouts Manhattan y no-Manhattan. Proponemos la combinación de Relative Location Features junto con características de textura para codificar las relaciones entre las diferentes clases de entidades. Usando estas características construimos un Conditional Random Field que nos permite estimar el mejor etiquetado en términos de minimización de energía. Los experimentos realizados sobre ambos tipos de documentos demuestran que la utilización de Relative Location Features ayuda a mejorar los resultados de la segmentación en documentos altamente estructurados, así como ofrecer resultados a la altura del estado del arte sobre documentos sin una estructura aparente.[EN] We present a generic layout analysis method devised to work in documents with both Manhattan and non-Mahnattan layouts. We propose to use Relative Location features combined with texture features to encode the relationships between the different class entities. Using these features we build a Conditional Random Field framework that allow us to obtain the best class configuration of an image in terms of energy minimization. The conducted experiments with Manhattan and non-Manhattan layouts prove that using Relative Location Features improves the segmentation results on highly structured documents, as well as results up to the state of the art on documents weakly structured.Cruz Fernández, F. (2012). Document segmentation using Relative Location Features. http://hdl.handle.net/10251/19219Archivo delegad

    Occode: an end-to-end machine learning pipeline for transcription of historical population censuses

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    Machine learning approaches achieve high accuracy for text recognition and are therefore increasingly used for the transcription of handwritten historical sources. However, using machine learning in production requires a streamlined end-to-end machine learning pipeline that scales to the dataset size, and a model that achieves high accuracy with few manual transcriptions. In addition, the correctness of the model results must be verified. This paper describes our lessons learned developing, tuning, and using the Occode end-to-end machine learning pipeline for transcribing 7,3 million rows with handwritten occupation codes in the Norwegian 1950 population census. We achieve an accuracy of 97% for the automatically transcribed codes, and we send 3% of the codes for manual verification. We verify that the occupation code distribution found in our result matches the distribution found in our training data which should be representative for the census as a whole. We believe our approach and lessons learned are useful for other transcription projects that plan to use machine learning in production. The source code is available at: https://github.com/uit-hdl/rhd-code

    Mathematical Expression Recognition based on Probabilistic Grammars

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    [EN] Mathematical notation is well-known and used all over the world. Humankind has evolved from simple methods representing countings to current well-defined math notation able to account for complex problems. Furthermore, mathematical expressions constitute a universal language in scientific fields, and many information resources containing mathematics have been created during the last decades. However, in order to efficiently access all that information, scientific documents have to be digitized or produced directly in electronic formats. Although most people is able to understand and produce mathematical information, introducing math expressions into electronic devices requires learning specific notations or using editors. Automatic recognition of mathematical expressions aims at filling this gap between the knowledge of a person and the input accepted by computers. This way, printed documents containing math expressions could be automatically digitized, and handwriting could be used for direct input of math notation into electronic devices. This thesis is devoted to develop an approach for mathematical expression recognition. In this document we propose an approach for recognizing any type of mathematical expression (printed or handwritten) based on probabilistic grammars. In order to do so, we develop the formal statistical framework such that derives several probability distributions. Along the document, we deal with the definition and estimation of all these probabilistic sources of information. Finally, we define the parsing algorithm that globally computes the most probable mathematical expression for a given input according to the statistical framework. An important point in this study is to provide objective performance evaluation and report results using public data and standard metrics. We inspected the problems of automatic evaluation in this field and looked for the best solutions. We also report several experiments using public databases and we participated in several international competitions. Furthermore, we have released most of the software developed in this thesis as open source. We also explore some of the applications of mathematical expression recognition. In addition to the direct applications of transcription and digitization, we report two important proposals. First, we developed mucaptcha, a method to tell humans and computers apart by means of math handwriting input, which represents a novel application of math expression recognition. Second, we tackled the problem of layout analysis of structured documents using the statistical framework developed in this thesis, because both are two-dimensional problems that can be modeled with probabilistic grammars. The approach developed in this thesis for mathematical expression recognition has obtained good results at different levels. It has produced several scientific publications in international conferences and journals, and has been awarded in international competitions.[ES] La notación matemática es bien conocida y se utiliza en todo el mundo. La humanidad ha evolucionado desde simples métodos para representar cuentas hasta la notación formal actual capaz de modelar problemas complejos. Además, las expresiones matemáticas constituyen un idioma universal en el mundo científico, y se han creado muchos recursos que contienen matemáticas durante las últimas décadas. Sin embargo, para acceder de forma eficiente a toda esa información, los documentos científicos han de ser digitalizados o producidos directamente en formatos electrónicos. Aunque la mayoría de personas es capaz de entender y producir información matemática, introducir expresiones matemáticas en dispositivos electrónicos requiere aprender notaciones especiales o usar editores. El reconocimiento automático de expresiones matemáticas tiene como objetivo llenar ese espacio existente entre el conocimiento de una persona y la entrada que aceptan los ordenadores. De este modo, documentos impresos que contienen fórmulas podrían digitalizarse automáticamente, y la escritura se podría utilizar para introducir directamente notación matemática en dispositivos electrónicos. Esta tesis está centrada en desarrollar un método para reconocer expresiones matemáticas. En este documento proponemos un método para reconocer cualquier tipo de fórmula (impresa o manuscrita) basado en gramáticas probabilísticas. Para ello, desarrollamos el marco estadístico formal que deriva varias distribuciones de probabilidad. A lo largo del documento, abordamos la definición y estimación de todas estas fuentes de información probabilística. Finalmente, definimos el algoritmo que, dada cierta entrada, calcula globalmente la expresión matemática más probable de acuerdo al marco estadístico. Un aspecto importante de este trabajo es proporcionar una evaluación objetiva de los resultados y presentarlos usando datos públicos y medidas estándar. Por ello, estudiamos los problemas de la evaluación automática en este campo y buscamos las mejores soluciones. Asimismo, presentamos diversos experimentos usando bases de datos públicas y hemos participado en varias competiciones internacionales. Además, hemos publicado como código abierto la mayoría del software desarrollado en esta tesis. También hemos explorado algunas de las aplicaciones del reconocimiento de expresiones matemáticas. Además de las aplicaciones directas de transcripción y digitalización, presentamos dos propuestas importantes. En primer lugar, desarrollamos mucaptcha, un método para discriminar entre humanos y ordenadores mediante la escritura de expresiones matemáticas, el cual representa una novedosa aplicación del reconocimiento de fórmulas. En segundo lugar, abordamos el problema de detectar y segmentar la estructura de documentos utilizando el marco estadístico formal desarrollado en esta tesis, dado que ambos son problemas bidimensionales que pueden modelarse con gramáticas probabilísticas. El método desarrollado en esta tesis para reconocer expresiones matemáticas ha obtenido buenos resultados a diferentes niveles. Este trabajo ha producido varias publicaciones en conferencias internacionales y revistas, y ha sido premiado en competiciones internacionales.[CA] La notació matemàtica és ben coneguda i s'utilitza a tot el món. La humanitat ha evolucionat des de simples mètodes per representar comptes fins a la notació formal actual capaç de modelar problemes complexos. A més, les expressions matemàtiques constitueixen un idioma universal al món científic, i s'han creat molts recursos que contenen matemàtiques durant les últimes dècades. No obstant això, per accedir de forma eficient a tota aquesta informació, els documents científics han de ser digitalitzats o produïts directament en formats electrònics. Encara que la majoria de persones és capaç d'entendre i produir informació matemàtica, introduir expressions matemàtiques en dispositius electrònics requereix aprendre notacions especials o usar editors. El reconeixement automàtic d'expressions matemàtiques té per objectiu omplir aquest espai existent entre el coneixement d'una persona i l'entrada que accepten els ordinadors. D'aquesta manera, documents impresos que contenen fórmules podrien digitalitzar-se automàticament, i l'escriptura es podria utilitzar per introduir directament notació matemàtica en dispositius electrònics. Aquesta tesi està centrada en desenvolupar un mètode per reconèixer expressions matemàtiques. En aquest document proposem un mètode per reconèixer qualsevol tipus de fórmula (impresa o manuscrita) basat en gramàtiques probabilístiques. Amb aquesta finalitat, desenvolupem el marc estadístic formal que deriva diverses distribucions de probabilitat. Al llarg del document, abordem la definició i estimació de totes aquestes fonts d'informació probabilística. Finalment, definim l'algorisme que, donada certa entrada, calcula globalment l'expressió matemàtica més probable d'acord al marc estadístic. Un aspecte important d'aquest treball és proporcionar una avaluació objectiva dels resultats i presentar-los usant dades públiques i mesures estàndard. Per això, estudiem els problemes de l'avaluació automàtica en aquest camp i busquem les millors solucions. Així mateix, presentem diversos experiments usant bases de dades públiques i hem participat en diverses competicions internacionals. A més, hem publicat com a codi obert la majoria del software desenvolupat en aquesta tesi. També hem explorat algunes de les aplicacions del reconeixement d'expressions matemàtiques. A més de les aplicacions directes de transcripció i digitalització, presentem dues propostes importants. En primer lloc, desenvolupem mucaptcha, un mètode per discriminar entre humans i ordinadors mitjançant l'escriptura d'expressions matemàtiques, el qual representa una nova aplicació del reconeixement de fórmules. En segon lloc, abordem el problema de detectar i segmentar l'estructura de documents utilitzant el marc estadístic formal desenvolupat en aquesta tesi, donat que ambdós són problemes bidimensionals que poden modelar-se amb gramàtiques probabilístiques. El mètode desenvolupat en aquesta tesi per reconèixer expressions matemàtiques ha obtingut bons resultats a diferents nivells. Aquest treball ha produït diverses publicacions en conferències internacionals i revistes, i ha sigut premiat en competicions internacionals.Álvaro Muñoz, F. (2015). Mathematical Expression Recognition based on Probabilistic Grammars [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/51665TESI

    A Multi-Decade Look at Black Female/White Male Interracial Marriages

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    abstract: The number of interracial marriages and multiracial individuals continues to increase rapidly in the United States (U.S. Census Bureau, 2010). Black Female (BF) /White Male (WM) marriages are increasing, but not as quickly as other interracial marriages (Wang, 2012) leaving this population void in social science literature available to social workers. Consequently, there is a lack of information available to understand factors that contribute to these couple identities and how they navigate in the monoracialized systems they encounter. This qualitative study explored how BF/WM partners married in different generational cohorts experience and navigate race and identity as a couple through video recorded interviews where couples shared their narrative as a dyad. The secondary data analyzed was originally collected through snowball and convenient sampling to find BF/WM married couples that were married different generational cohorts living in the Phoenix area. Couples were asked to respond to starter questions (Linhorst, 2002) that encouraged them to share experiences as a couple interacting with community, social, and family systems. Ecological systems framework and social construction were used to guide analysis. Results from the multimodal transcript analysis and detailed review of the video data found themes of invisibility of the couples' relationships from community and family. Differences between cohorts were identified with movement from separation of racial identities within the couple identity to an infusion of both identities represented within the couple. Additionally, insights into the benefits of videography as a data collection method and its usefulness in to connecting social work research to practice were identified and align with the NASW Cultural Competence standards (NASW, 2001).Dissertation/ThesisDoctoral Dissertation Social Work 201

    Engaging an Unengaged Demographic: A Phenomenological Study of Christian Millennials’ Engagement with Religious and Nonreligious Memberships

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    Church membership is declining worldwide. Studies show that Millennials are less likely to belong to a church than previous generations. Even among churched Millennials, only 48% of church-attending Millennials are church members. Simultaneously, organizations such as health, fitness, and social clubs are seeing an increase in Millennial membership growth. Brand loyalty is high among Millennials, but church loyalty is low even among practicing Christian Millennials who attend church at least once per month. The purpose of this qualitative phenomenological study was to explore how practicing Christian Millennials understand the nature of engagement with church and nonreligious memberships and the factors that shape those commitments. Practicing Christian Millennials were generally defined as anyone born between 1981 and 1996 who self-identified as a Christian and attended a trinitarian Protestant church in the United States at least once per month. The methodology guiding this study was Husserl’s phenomenological approach to gaining insight into the phenomenon of Christian Millennials’ engagement rates with religious and nonreligious memberships
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