A bimodal crowdsourcing platform for demographic historical manuscripts

Ponència presentada al First International Conference on Digital Access to Textual Cultural Heritage celebrada del 19 al 20 de maig de 2014 a MadridIn this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts

Handwritten Text Recognition for Historical Documents in the tranScriptorium Project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

Word graphs size impact on the performance of handwriting document applications

[EN] Two document processing applications are con- sidered: computer-assisted transcription of text images (CATTI) and Keyword Spotting (KWS), for transcribing and indexing handwritten documents, respectively. Instead of working directly on the handwriting images, both of them employ meta-data structures called word graphs (WG), which are obtained using segmentation-free hand- written text recognition technology based on N-gram lan- guage models and hidden Markov models. A WG contains most of the relevant information of the original text (line) image required by CATTI and KWS but, if it is too large, the computational cost of generating and using it can become unafordable. Conversely, if it is too small, relevant information may be lost, leading to a reduction of CATTI or KWS performance. We study the trade-off between WG size and performance in terms of effectiveness and effi- ciency of CATTI and KWS. Results show that small, computationally cheap WGs can be used without loosing the excellent CATTI and KWS performance achieved with huge WGs.Work partially supported by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMAMATER, by the Spanish MECD as part of the Valorization and I+D+I Resources program of VLC/CAMPUS in the International Excellence Campus program, and through the EU projects: HIMANIS (JPICH programme, Spanish Grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, Grant Ref. 674943).Toselli ., AH.; Romero Gómez, V.; Vidal, E. (2017). Word graphs size impact on the performance of handwriting document applications. Neural Computing and Applications. 28(9):2477-2487. https://doi.org/10.1007/s00521-016-2336-2S24772487289Amengual JC, Vidal E (1998) Efficient error-correcting Viterbi parsing. IEEE Trans Pattern Anal Mach Intell 20(10):1109–1116Bazzi I, Schwartz R, Makhoul J (1999) An omnifont open-vocabulary OCR system for English and Arabic. 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Advances on the Transcription of Historical Manuscripts based on Multimodality, Interactivity and Crowdsourcing

Natural Language Processing (NLP) is an interdisciplinary research field of Computer Science, Linguistics, and Pattern Recognition that studies, among others, the use of human natural languages in Human-Computer Interaction (HCI). Most of NLP research tasks can be applied for solving real-world problems. This is the case of natural language recognition and natural language translation, that can be used for building automatic systems for document transcription and document translation. Regarding digitalised handwritten text documents, transcription is used to obtain an easy digital access to the contents, since simple image digitalisation only provides, in most cases, search by image and not by linguistic contents (keywords, expressions, syntactic or semantic categories). Transcription is even more important in historical manuscripts, since most of these documents are unique and the preservation of their contents is crucial for cultural and historical reasons. The transcription of historical manuscripts is usually done by paleographers, who are experts on ancient script and vocabulary. Recently, Handwritten Text Recognition (HTR) has become a common tool for assisting paleographers in their task, by providing a draft transcription that they may amend with more or less sophisticated methods. This draft transcription is useful when it presents an error rate low enough to make the amending process more comfortable than a complete transcription from scratch. Thus, obtaining a draft transcription with an acceptable low error rate is crucial to have this NLP technology incorporated into the transcription process. The work described in this thesis is focused on the improvement of the draft transcription offered by an HTR system, with the aim of reducing the effort made by paleographers for obtaining the actual transcription on digitalised historical manuscripts. This problem is faced from three different, but complementary, scenarios: · Multimodality: The use of HTR systems allow paleographers to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is to obtain the draft transcription by dictating the contents to an Automatic Speech Recognition (ASR) system. When both sources (image and speech) are available, a multimodal combination is possible and an iterative process can be used in order to refine the final hypothesis. · Interactivity: The use of assistive technologies in the transcription process allows one to reduce the time and human effort required for obtaining the actual transcription, given that the assistive system and the palaeographer cooperate to generate a perfect transcription. Multimodal feedback can be used to provide the assistive system with additional sources of information by using signals that represent the whole same sequence of words to transcribe (e.g. a text image, and the speech of the dictation of the contents of this text image), or that represent just a word or character to correct (e.g. an on-line handwritten word). · Crowdsourcing: Open distributed collaboration emerges as a powerful tool for massive transcription at a relatively low cost, since the paleographer supervision effort may be dramatically reduced. Multimodal combination allows one to use the speech dictation of handwritten text lines in a multimodal crowdsourcing platform, where collaborators may provide their speech by using their own mobile device instead of using desktop or laptop computers, which makes it possible to recruit more collaborators.El Procesamiento del Lenguaje Natural (PLN) es un campo de investigación interdisciplinar de las Ciencias de la Computación, Lingüística y Reconocimiento de Patrones que estudia, entre otros, el uso del lenguaje natural humano en la interacción Hombre-Máquina. La mayoría de las tareas de investigación del PLN se pueden aplicar para resolver problemas del mundo real. Este es el caso del reconocimiento y la traducción del lenguaje natural, que se pueden utilizar para construir sistemas automáticos para la transcripción y traducción de documentos. En cuanto a los documentos manuscritos digitalizados, la transcripción se utiliza para facilitar el acceso digital a los contenidos, ya que la simple digitalización de imágenes sólo proporciona, en la mayoría de los casos, la búsqueda por imagen y no por contenidos lingüísticos. La transcripción es aún más importante en el caso de los manuscritos históricos, ya que la mayoría de estos documentos son únicos y la preservación de su contenido es crucial por razones culturales e históricas. La transcripción de manuscritos históricos suele ser realizada por paleógrafos, que son personas expertas en escritura y vocabulario antiguos. Recientemente, los sistemas de Reconocimiento de Escritura (RES) se han convertido en una herramienta común para ayudar a los paleógrafos en su tarea, la cual proporciona un borrador de la transcripción que los paleógrafos pueden corregir con métodos más o menos sofisticados. Este borrador de transcripción es útil cuando presenta una tasa de error suficientemente reducida para que el proceso de corrección sea más cómodo que una completa transcripción desde cero. Por lo tanto, la obtención de un borrador de transcripción con una baja tasa de error es crucial para que esta tecnología de PLN sea incorporada en el proceso de transcripción. El trabajo descrito en esta tesis se centra en la mejora del borrador de transcripción ofrecido por un sistema RES, con el objetivo de reducir el esfuerzo realizado por los paleógrafos para obtener la transcripción de manuscritos históricos digitalizados. Este problema se enfrenta a partir de tres escenarios diferentes, pero complementarios: · Multimodalidad: El uso de sistemas RES permite a los paleógrafos acelerar el proceso de transcripción manual, ya que son capaces de corregir en un borrador de la transcripción. Otra alternativa es obtener el borrador de la transcripción dictando el contenido a un sistema de Reconocimiento Automático de Habla. Cuando ambas fuentes están disponibles, una combinación multimodal de las mismas es posible y se puede realizar un proceso iterativo para refinar la hipótesis final. · Interactividad: El uso de tecnologías asistenciales en el proceso de transcripción permite reducir el tiempo y el esfuerzo humano requeridos para obtener la transcripción correcta, gracias a la cooperación entre el sistema asistencial y el paleógrafo para obtener la transcripción perfecta. La realimentación multimodal se puede utilizar en el sistema asistencial para proporcionar otras fuentes de información adicionales con señales que representen la misma secuencia de palabras a transcribir (por ejemplo, una imagen de texto, o la señal de habla del dictado del contenido de dicha imagen de texto), o señales que representen sólo una palabra o carácter a corregir (por ejemplo, una palabra manuscrita mediante una pantalla táctil). · Crowdsourcing: La colaboración distribuida y abierta surge como una poderosa herramienta para la transcripción masiva a un costo relativamente bajo, ya que el esfuerzo de supervisión de los paleógrafos puede ser drásticamente reducido. La combinación multimodal permite utilizar el dictado del contenido de líneas de texto manuscrito en una plataforma de crowdsourcing multimodal, donde los colaboradores pueden proporcionar las muestras de habla utilizando su propio dispositivo móvil en lugar de usar ordenadores,El Processament del Llenguatge Natural (PLN) és un camp de recerca interdisciplinar de les Ciències de la Computació, la Lingüística i el Reconeixement de Patrons que estudia, entre d'altres, l'ús del llenguatge natural humà en la interacció Home-Màquina. La majoria de les tasques de recerca del PLN es poden aplicar per resoldre problemes del món real. Aquest és el cas del reconeixement i la traducció del llenguatge natural, que es poden utilitzar per construir sistemes automàtics per a la transcripció i traducció de documents. Quant als documents manuscrits digitalitzats, la transcripció s'utilitza per facilitar l'accés digital als continguts, ja que la simple digitalització d'imatges només proporciona, en la majoria dels casos, la cerca per imatge i no per continguts lingüístics (paraules clau, expressions, categories sintàctiques o semàntiques). La transcripció és encara més important en el cas dels manuscrits històrics, ja que la majoria d'aquests documents són únics i la preservació del seu contingut és crucial per raons culturals i històriques. La transcripció de manuscrits històrics sol ser realitzada per paleògrafs, els quals són persones expertes en escriptura i vocabulari antics. Recentment, els sistemes de Reconeixement d'Escriptura (RES) s'han convertit en una eina comuna per ajudar els paleògrafs en la seua tasca, la qual proporciona un esborrany de la transcripció que els paleògrafs poden esmenar amb mètodes més o menys sofisticats. Aquest esborrany de transcripció és útil quan presenta una taxa d'error prou reduïda perquè el procés de correcció siga més còmode que una completa transcripció des de zero. Per tant, l'obtenció d'un esborrany de transcripció amb un baixa taxa d'error és crucial perquè aquesta tecnologia del PLN siga incorporada en el procés de transcripció. El treball descrit en aquesta tesi se centra en la millora de l'esborrany de la transcripció ofert per un sistema RES, amb l'objectiu de reduir l'esforç realitzat pels paleògrafs per obtenir la transcripció de manuscrits històrics digitalitzats. Aquest problema s'enfronta a partir de tres escenaris diferents, però complementaris: · Multimodalitat: L'ús de sistemes RES permet als paleògrafs accelerar el procés de transcripció manual, ja que són capaços de corregir un esborrany de la transcripció. Una altra alternativa és obtenir l'esborrany de la transcripció dictant el contingut a un sistema de Reconeixement Automàtic de la Parla. Quan les dues fonts (imatge i parla) estan disponibles, una combinació multimodal és possible i es pot realitzar un procés iteratiu per refinar la hipòtesi final. · Interactivitat: L'ús de tecnologies assistencials en el procés de transcripció permet reduir el temps i l'esforç humà requerits per obtenir la transcripció real, gràcies a la cooperació entre el sistema assistencial i el paleògraf per obtenir la transcripció perfecta. La realimentació multimodal es pot utilitzar en el sistema assistencial per proporcionar fonts d'informació addicionals amb senyals que representen la mateixa seqüencia de paraules a transcriure (per exemple, una imatge de text, o el senyal de parla del dictat del contingut d'aquesta imatge de text), o senyals que representen només una paraula o caràcter a corregir (per exemple, una paraula manuscrita mitjançant una pantalla tàctil). · Crowdsourcing: La col·laboració distribuïda i oberta sorgeix com una poderosa eina per a la transcripció massiva a un cost relativament baix, ja que l'esforç de supervisió dels paleògrafs pot ser reduït dràsticament. La combinació multimodal permet utilitzar el dictat del contingut de línies de text manuscrit en una plataforma de crowdsourcing multimodal, on els col·laboradors poden proporcionar les mostres de parla utilitzant el seu propi dispositiu mòbil en lloc d'utilitzar ordinadors d'escriptori o portàtils, la qual cosa permet ampliar el nombrGranell Romero, E. (2017). Advances on the Transcription of Historical Manuscripts based on Multimodality, Interactivity and Crowdsourcing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86137TESI

The use of new technologies to access to handwritten historical information in digital form. Galeón Project

Español: La investigación histórica en archivos obliga a realizar un amplio trabajo de revisión de miles de documentos que, en muchos casos, no tienen relación con el tema de estudio, generando un importante gasto en tiempo y recursos. Para dar respuesta a este problema en relación al estudio del patrimonio arqueológico subacuático, desde el CAS-IAPH se ha ideado el Proyecto Galeón, cuyo objetivo es desarrollar soluciones innovadoras para consultar grandes conjuntos digitalizados de documentos históricos manuscritos. Actualmente no es posible la transcripción automatizada de un gran volumen de imágenes de documentos manuscritos, pero el desarrollo tecnológico en el campo del reconocimiento formal de palabras, puede simplificar este proceso. Para ello se ha ideado un modelo teórico de Búsqueda de Palabras Claves (BPC) basado en Grafos de Palabras (GP), que, además de para el patrimonio cultural marítimo, podría utilizarse para otros temas de investigación. Inglés: Historical research in archives forces to realize an extensive work of reviewing thousands of documents that, in many cases, have no connection with the subject matter, generating a significant expenditure of time and resources. To address this problem in relation to the study of underwater archaeological heritage, from the CAS-IAPH has been devised the Galleon Project, which aims to develop innovative solutions to query large sets of historical documents digitized manuscripts. Nowadays It is not possible the automated transcription of a large volume of images from handwritten documents, but the development in the field of formal recognition of words, can simplify this process. For this we have developed a theoretical model to identify Keywords based on Graphs of Words (GP), which, as well as in the maritime cultural heritage, could be used for any research topic

La reconnaissance de l’écriture manuscrite hors ligne. Applicabilité à la transcription et l’indexation d’un fonds notarial des Archives cantonales jurassiennes

Ce travail étudie un ensemble d’options et de méthodes de transcription et d’indexation pour un fonds notarial des Archives cantonales jurassiennes (ArCJ), en portant une attention particulière à la reconnaissance de l’écriture manuscrite hors ligne. Les recherches dans cette discipline ont fait de grands progrès depuis leurs débuts dans les années 60. S’inspirant des résultats prometteurs de la reconnaissance automatique de la parole, de nombreux outils ont été développés pour tenter d’égaler et de surpasser les capacités humaines de déchiffrement. De plus en plus d’institutions numérisent d’importantes quantités de documents manuscrits, qui sont ensuite mis en ligne à disposition du public. La grande majorité de ces documents attend toujours d'être transcrite et indexée pour offrir aux chercheurs un meilleur accès à leurs contenus. Après une définition de la problématique de ce travail, une première partie fait l’état de l’art de la reconnaissance de l’écriture manuscrite d’après la littérature scientifique la plus récente sur le sujet. L’historique de la discipline est retracé, les principes généraux et le fonctionnement des programmes sont expliqués. Pour déterminer quelles sont les pratiques actuellement en vigueur, une enquête auprès d’autres institutions est menée. Une seconde partie réalise un inventaire et décrit le fonds concerné par ce travail. Il s’agit de répertoires de notaires, documents très importants pour avoir accès aux minutes des différentes études. Les Archives cantonales jurassiennes et leur stratégie de numérisation sont présentées. Les registres sont analysés en détail, tant sur le plan du support que sur celui du contenu. Une typologie des difficultés est également établie. Finalement, la compatibilité du fonds avec les méthodes automatiques, manuelles ou semi-automatiques retenues est évaluée pour déterminer si elles sont en mesure d’apporter une solution aux défis et difficultés que suscite l’indexation des répertoires. A partir de ces observations où théorie et pratique se côtoient, des recommandations sont faites aux Archives cantonales jurassiennes, afin de les aider à déterminer quelle serait pour elles la manière la plus rentable de transcrire et indexer leurs registres

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

[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

Probabilistic multi-word spotting in handwritten text images

[EN] Keyword spotting techniques are becoming cost-effective solutions for information retrieval in handwritten documents. We explore the extension of the single-word, line-level probabilistic indexing approach described in our previous works to allow for page-level search of queries consisting in Boolean combinations of several single-keywords. We propose heuristic rules to combine the single-word relevance probabilities into probabilistically consistent confidence scores of the multi-word boolean combinations. An empirical study, also presented in this paper, evaluates the search performance of word-pair queries involving AND and OR Boolean operations. Results of this study support the proposed approach and clearly show its effectiveness. Finally, a web-based demonstration system based on the proposed methods is presented.This work was partially supported by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMAMATER, Spanish MEC under Grant FPU13/06281, and through the EU projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, Grant Ref. 674943).Toselli, AH.; Vidal, E.; Puigcerver, J.; Noya-García, E. (2019). Probabilistic multi-word spotting in handwritten text images. Pattern Analysis and Applications. 22(1):23-32. https://doi.org/10.1007/s10044-018-0742-zS2332221Andreu Sanchez J, Romero V, Toselli A, Vidal E (2014) ICFHR2014 competition on handwritten text recognition on transcriptorium datasets (HTRtS). In: 14th International conference on frontiers in handwriting recognition (ICFHR), 2014, pp 785–790Bazzi I, Schwartz R, Makhoul J (1999) An omnifont open-vocabulary OCR system for English and Arabic. IEEE Trans Pattern Anal Mach Intell 21(6):495–504Bluche T, Hamel S, Kermorvant C, Puigcerver J, Stutzmann D, Toselli AH, Vidal E (2017) Preparatory KWS experiments for large-scale indexing of a vast medieval manuscript collection in the hIMANIS Project. In: 14th International conference on document analysis and recognition (ICDAR). (Accepted)Bluche T, Hamel S, Kermorvant C, Puigcerver J, Stutzmann D, Toselli AH, Vidal E (2017) Preparatory kws experiments for large-scale indexing of a vast medieval manuscript collection in the himanis project. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol. 01, pp 311–316. https://doi.org/10.1109/ICDAR.2017.59Boole G (1854) An investigation of the laws of thought on which are founded the mathematical theories of logic and probabilities. Macmillan, New YorkCauser T, Wallace V (2012) Building a volunteer community: results and findings from Transcribe Bentham. Digital Humanities Quarterly 6España-Boquera S, Castro-Bleda MJ, Gorbe-Moya J, Zamora-Martinez F (2011) Improving offline handwritten text recognition with hybrid hmm/ann models. IEEE Trans Pattern Anal Mach Intell 33(4):767–779. https://doi.org/10.1109/TPAMI.2010.141Fischer A, Wuthrich M, Liwicki M, Frinken V, Bunke H, Viehhauser G, Stolz M (2009) Automatic transcription of handwritten medieval documents. In: 15th International conference on virtual systems and multimedia, 2009. VSMM ’09, pp 137–142. https://doi.org/10.1109/VSMM.2009.26Fréchet M (1935) Généralisations du théorème des probabilités totales. Seminarjum MatematyczneFréchet M (1951) Sur les tableaux de corrélation dont les marges sont données. Ann Univ Lyon 3 $$^{\wedge }$$ ∧ e ser Sci Sect A 14:53–77Graves A, Liwicki M, Fernández S, Bertolami R, Bunke H, Schmidhuber J (2009) A novel connectionist system for unconstrained handwriting recognition. IEEE Trans Pattern Anal Mach Intell 31(5):855–868Jelinek F (1998) Statistical methods for speech recognition. MIT Press, CambridgeKneser R, Ney H (1995) Improved backing-off for N-gram language modeling. In: International conference on acoustics, speech and signal processing (ICASSP ’95), IEEE Computer Society, Los Alamitos, vol. 1, pp. 181–184, https://doi.org/10.1109/ICASSP.1995.479394Kozielski M, Forster J, Ney H (2012) Moment-based image normalization for handwritten text recognition. In: Proceedings of the 2012 international conference on frontiers in handwriting recognition, ICFHR ’12, pp 256–261. IEEE Computer Society, Washington. https://doi.org/10.1109/ICFHR.2012.236Lavrenko V, Rath TM, Manmatha R (2004) Holistic word recognition for handwritten historical documents. In: First Proceedings of international workshop on document image analysis for libraries, 2004, pp 278–287. https://doi.org/10.1109/DIAL.2004.1263256Manning CD, Raghavan P, Schutze H (2008) Introduction to information retrieval. Cambridge University Press, New YorkMarti UV, Bunke H (2002) The iam-database: an english sentence database for offline handwriting recognition. Int J Doc Anal Recogn 5:39–46. https://doi.org/10.1007/s100320200071Noya-García E, Toselli AH, Vidal E (2017) Simple and effective multi-word query spotting in handwritten text images, pp 76–84. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-58838-4_9Pratikakis I, Zagoris K, Gatos B, Louloudis G, Stamatopoulos N (2014) ICFHR 2014 competition on handwritten keyword spotting (h-kws 2014). In: 14th International conference on frontiers in handwriting recognition (ICFHR), 2014, pp 814–819Puigcerver J, Toselli AH, Vidal E (2015) Icdar2015 competition on keyword spotting for handwritten documents. In: 13th international conference on document analysis and recognition (ICDAR), 2015, pp 1176–1180Riba P, Almazn J, Forns A, Fernndez-Mota D, Valveny E, Llads J (2014) e-crowds: a mobile platform for browsing and searching in historical demography-related manuscripts. In: 14th International conference on frontiers in handwriting recognition (ICFHR), 2014, pp 228–233. https://doi.org/10.1109/ICFHR.2014.46Robertson S (2008) A new interpretation of average precision. In: Proceedings of the international ACM SIGIR conference on research and development in information retrieval (SIGIR ’08), pp 689–690. ACM, New York. https://doi.org/10.1145/1390334.1390453Romero V, Toselli AH, Vidal E (2012) Multimodal interactive handwritten text transcription. Series in machine perception and artificial intelligence (MPAI). World Scientific Publishing, SingaporeSánchez JA, Romero V, Toselli AH, Vidal E (2016) ICFHR2016 competition on handwritten text recognition on the READ dataset. In: 15th International conference on frontiers in handwriting recognition (ICFHR’16), pp 630–635. https://doi.org/10.1109/ICFHR.2016.0120Toselli A, Vidal E (2015) Handwritten text recognition results on the Bentham collection with improved classical N-Gram-HMM methods. In: 3rd International workshop on historical document imaging and processing (HIP15), pp 15–22Toselli AH, Juan A, Keysers D, González J, Salvador I, Ney H, Vidal E, Casacuberta F (2004) Integrated Handwriting Recognition and Interpretation using Finite-State Models. Int J Pattern Recogn Artif Intell 18(4):519–539Toselli AH, Vidal E, Romero V, Frinken V (2016) HMM word graph based keyword spotting in handwritten document images. 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Working paper 2004-09 Department of Statistics and Actuarial Science–University of Waterlo

Word-Graph Based Applications for Handwriting Documents: Impact of Word-Graph Size on Their Performances

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8 29Computer Assisted Transcription of Text Images (CATTI) and Key-Word Spotting (KWS) applications aim at transcribing and indexing handwritten documents respectively. They both are approached by means of Word Graphs (WG) obtained using segmentation-free handwritten text recognition technology based on N-gram Language Models and Hidden Markov Models. A large WG contains most of the relevant information of the original text (line) image needed for CATTI and KWS but, if it is too large, the computational cost of generating and using it can become unaffordable. Conversely, if it is too small, relevant information may be lost, leading to a reduction of CATTI/KWS in performance accuracy. We study the trade-off between WG size and CATTI &KWS performance in terms of effectiveness and efficiency. Results show that small, computationally cheap WGs can be used without loosing the excellent CATTI/KWS performance achieved with huge WGs.Work partially supported by the Spanish MICINN projects STraDA (TIN2012-37475-C02-01) and by the EU 7th FP tranScriptorium project (Ref:600707).Toselli, AH.; Romero Gómez, V.; Vidal Ruiz, E. (2015). Word-Graph Based Applications for Handwriting Documents: Impact of Word-Graph Size on Their Performances. En Pattern Recognition and Image Analysis. Springer. 253-261. https://doi.org/10.1007/978-3-319-19390-8_29S253261Romero, V., Toselli, A.H., Vidal, E.: Multimodal Interactive Handwritten Text Transcription. Series in Machine Perception and Artificial Intelligence (MPAI). World Scientific Publishing, Singapore (2012)Toselli, A.H., Vidal, E., Romero, V., Frinken, V.: Word-graph based keyword spotting and indexing of handwritten document images. Technical report, Universitat Politècnica de València (2013)Oerder, M., Ney, H.: Word graphs: an efficient interface between continuous-speech recognition and language understanding. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 119–122, April 1993Bazzi, I., Schwartz, R., Makhoul, J.: An omnifont open-vocabulary OCR system for English and Arabic. IEEE Trans. Pattern Anal. Mach. Intell. 21(6), 495–504 (1999)Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1998)Ström, N.: Generation and minimization of word graphs in continuous speech recognition. In: Proceedings of IEEE Workshop on ASR 1995, Snowbird, Utah, pp. 125–126 (1995)Ortmanns, S., Ney, H., Aubert, X.: A word graph algorithm for large vocabulary continuous speech recognition. Comput. Speech Lang. 11(1), 43–72 (1997)Wessel, F., Schluter, R., Macherey, K., Ney, H.: Confidence measures for large vocabulary continuous speech recognition. IEEE Trans. Speech Audio Process. 9(3), 288–298 (2001)Robertson, S.: A new interpretation of average precision. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 689–690. ACM, USA (2008)Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, USA (2008)Romero, V., Toselli, A.H., Rodríguez, L., Vidal, E.: Computer assisted transcription for ancient text images. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1182–1193. Springer, Heidelberg (2007)Fischer, A., Wuthrich, M., Liwicki, M., Frinken, V., Bunke, H., Viehhauser, G., Stolz, M.: Automatic transcription of handwritten medieval documents. In: 15th International Conference on Virtual Systems and Multimedia, VSMM 2009, pp. 137–142 (2009)Pesch, H., Hamdani, M., Forster, J., Ney, H.: Analysis of preprocessing techniques for latin handwriting recognition. In: ICFHR, pp. 280–284 (2012)Evermann, G.: Minimum Word Error Rate Decoding. Ph.D. thesis, Churchill College, University of Cambridge (1999