A Web-Based Demo to Interactive Multimodal Transcription of Historic Text images

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04346-8_58[EN] Paleography experts spend many hours transcribing historic documents, and state-of-the-art handwritten text recognition systems are not suitable for performing this task automatically. In this paper we present the modifications oil a previously developed interactive framework for transcription of handwritten text. This system, rather than full automation, aimed at assisting the user with the recognition-transcription process.This work has been supported by the EC (FEDER), the Spanish MEC under grant TIN2006-15694-C02-01 and the research programme Consolider Ingenio 2010 MIPRCV (CSD2007-00018) and by the UPV (FPI fellowship 2006-04).Romero Gómez, V.; Leiva Torres, LA.; Alabau Gonzalvo, V.; Toselli, AH.; Vidal Ruiz, E. (2009). A Web-Based Demo to Interactive Multimodal Transcription of Historic Text images. En Research and Advanced Technology for Digital Libraries: 13th European Conference, ECDL 2009, Corfu, Greece, September 27 - October 2, 2009. Proceedings. Springer Verlag (Germany). 459-460. https://doi.org/10.1007/978-3-642-04346-8_58S459460Toselli, A.H., et al.: Computer assisted transcription of handwritten text. In: Proc. of ICDAR 2007, pp. 944–948. IEEE Computer Society, Los Alamitos (2007)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)Toselli, A.H., et al.: Computer assisted transcription of text images and multimodal interaction. In: Popescu-Belis, A., Stiefelhagen, R. (eds.) MLMI 2008. LNCS, vol. 5237, pp. 296–308. Springer, Heidelberg (2008)Romero, V.: et al.: Interactive multimodal transcription of text images using a web-based demo system. In: Proc. of the IUI, Florida, pp. 477–478 (2009)Romero, V., et al.: Improvements in the computer assisted transciption system of handwritten text images. In: Proc. of the PRIS 2008, pp. 103–112 (2008

Multimodal Interactive Parsing

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38628-2_57Probabilistic parsing is a fundamental problem in Computational Linguistics, whose goal is obtaining a syntactic structure associated to a sentence according to a probabilistic grammatical model. Recently, an interactive framework for probabilistic parsing has been introduced, in which the user and the system cooperate to generate error-free parse trees. In an early prototype developed according to this interactive parsing technology, user feedback was provided by means of mouse actions and keyboard strokes. Here we augment the interaction style with support for (non-deterministic) natural handwritten recognition, and provide confidence measures as a visual aid to ease the correction process. Handwriting input seems to be a modality specially suitable for parsing, since the vocabulary size involved in the recognition of syntactic labels is fairly limited and thus intuitively errors should be small. However, errors may increase as handwriting quality (i.e., calligraphy) degrades. To solve this problem, we introduce a late fusion approach that leverages both on-line and off-line information, corresponding to pen strokes and contextual information from the parse trees. We demonstrate that late fusion can effectively help to disambiguate user intention and improve system accuracy.This research has received funding from the EC’s 7th Framework Programme (FP7/2007-13) under grant agreement No.287576- CasMaCat; from the Spanish MEC under the STraDA project (TIN2012-37475- C02-01) and the MITTRAL project (TIN2009-14633-C03-01); from the GV under the Prometeo project; and from the Universidad del Cauca (Colombia)Benedí Ruiz, JM.; Sánchez Peiró, JA.; Leiva, LA.; Sánchez Sáez, R.; Maca, M. (2013). Multimodal Interactive Parsing. En Pattern Recognition and Image Analysis. Springer. 484-491. https://doi.org/10.1007/978-3-642-38628-2_57S484491Afonso, S., Bick, E., Haber, R., Santos, D.: Floresta sintá(c)tica: a treebank for portuguese. In: Proc. LREC, pp. 1698–1703 (2002)Brants, T., Plaehn, O.: Interactive corpus annotation. In: Proc. LREC (2000)Guyon, I., Schomaker, L., Plamondon, R., Liberman, M., Janet, S.: UNIPEN project of on-line data exchange and recognizer benchmarks. In: Proc. ICPR, pp. 29–33 (1994)Lease, M., Charniak, E., Johnson, M., McClosky, D.: A look at parsing and its applications. In: Proc. AAAI, pp. 1642–1645 (2006)Marcus, M.P., Santorini, B., Marcinkiewicz, M.A.: Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics 19(2), 313–330 (1993)Ortiz, D., Leiva, L.A., Alabau, V., Casacuberta, F.: Interactive machine translation using a web-based architecture. In: Proc. IUI, pp. 423–425 (2010)Romero, V., Leiva, L.A., Toselli, A.H., Vidal, E.: Interactive multimodal transcription of text images using a web-based demo system. In: Proc. IUI, pp. 477–478 (2009)Sánchez-Sáez, R., Leiva, L.A., Sánchez, J.A., Benedí, J.M.: Interactive predictive parsing using a web-based architecture. In: Proc. NAACL-HLT, pp. 37–40 (2010)Sánchez-Sáez, R., Sánchez, J.A., Benedí, J.M.: Interactive predictive parsing. In: Proc. IWPT, pp. 222–225 (2009)Sánchez-Sáez, R., Sánchez, J.A., Benedí, J.M.: Confidence measures for error discrimination in an interactive predictive parsing framework. In: Proc. COLING, pp. 1220–1228 (2010

Diverse Contributions to Implicit Human-Computer Interaction

Cuando las personas interactúan con los ordenadores, hay mucha información que no se proporciona a propósito. Mediante el estudio de estas interacciones implícitas es posible entender qué características de la interfaz de usuario son beneficiosas (o no), derivando así en implicaciones para el diseño de futuros sistemas interactivos. La principal ventaja de aprovechar datos implícitos del usuario en aplicaciones informáticas es que cualquier interacción con el sistema puede contribuir a mejorar su utilidad. Además, dichos datos eliminan el coste de tener que interrumpir al usuario para que envíe información explícitamente sobre un tema que en principio no tiene por qué guardar relación con la intención de utilizar el sistema. Por el contrario, en ocasiones las interacciones implícitas no proporcionan datos claros y concretos. Por ello, hay que prestar especial atención a la manera de gestionar esta fuente de información. El propósito de esta investigación es doble: 1) aplicar una nueva visión tanto al diseño como al desarrollo de aplicaciones que puedan reaccionar consecuentemente a las interacciones implícitas del usuario, y 2) proporcionar una serie de metodologías para la evaluación de dichos sistemas interactivos. Cinco escenarios sirven para ilustrar la viabilidad y la adecuación del marco de trabajo de la tesis. Resultados empíricos con usuarios reales demuestran que aprovechar la interacción implícita es un medio tanto adecuado como conveniente para mejorar de múltiples maneras los sistemas interactivos.Leiva Torres, LA. (2012). Diverse Contributions to Implicit Human-Computer Interaction [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17803Palanci

Multimodal output combination for transcribing historical handwritten documents

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23192-1_21Transcription of digitalised historical documents is an interesting task in the document analysis area. This transcription can be achieved by using Handwritten Text Recognition (HTR) on digitalised pages or by using Automatic Speech Recognition (ASR) on the dictation of contents. Moreover, another option is using both systems in a multimodal combination to obtain a draft transcription, given that combining the outputs of different recognition systems will generally improve the recognition accuracy. In this work, we present a new combination method based on Confusion Network. We check its effectiveness for transcribing a Spanish historical book. Results on both unimodal combination with different optical (for HTR) and acoustic (for ASR) models, and multimodal combination, show a relative reduction of Word and Character Error Rate of 14.3% and 16.6%, respectively, over the HTR baseline.Work partially supported by European Union -7th FP, under grant 600707 (tranScriptorium), and by the Spanish MEC under projects STraDA (TIN2012-37475-C02-01), Active2Trans (TIN2012-31723), and SmartWays (RTC-2014-1466-4).Granell Romero, E.; Martínez-Hinarejos, C. (2015). Multimodal output combination for transcribing historical handwritten documents. En Computer Analysis of Images and Patterns. Springer. 246-260. https://doi.org/10.1007/978-3-319-23192-1_21S246260Alabau, V., Martínez-Hinarejos, C.D., Romero, V., Lagarda, A.L.: An iterative multimodal framework for the transcription of handwritten historical documents. 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Contex-aware gestures for mixed-initiative text editings UIs

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019[EN] This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.This work has been supported by the European Commission through the 7th Framework Program (tranScriptorium: FP7- ICT-2011-9, project 600707 and CasMaCat: FP7-ICT-2011-7, project 287576). It has also been supported by the Spanish MINECO under grant TIN2012-37475-C02-01 (STraDa), and the Generalitat Valenciana under grant ISIC/2012/004 (AMIIS).Leiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. https://doi.org/10.1093/iwc/iwu019S675696276Alabau V. Leiva L. A. Transcribing Handwritten Text Images with a Word Soup Game. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 2012.Alabau V. Rodríguez-Ruiz L. Sanchis A. Martínez-Gómez P. Casacuberta F. 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