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

Multimodal Crowdsourcing for Transcribing Handwritten Documents

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Transcription of handwritten documents is an important research topic for multiple applications, such as document classification or information extraction. In the case of historical documents, their transcription allows to preserve cultural heritage because of the amount of historical data contained in those documents. The transcription process can employ state-of-the-art handwritten text recognition systems in order to obtain an initial transcription. This transcription is usually not good enough for the quality standards, but that may speed up the final transcription of the expert. In this framework, the use of collaborative transcription applications (crowdsourcing) has risen in the recent years, but these platforms are mainly limited by the use of non-mobile devices. Thus, the recruiting initiatives get reduced to a smaller set of potential volunteers. In this paper, an alternative that allows the use of mobile devices is presented. The proposal consists of using speech dictation of handwritten text lines. Then, by using multimodal combination of speech and handwritten text images, a draft transcription can be obtained, presenting more quality than that obtained by only using handwritten text recognition. The speech dictation platform is implemented as a mobile device application, which allows for a wider range of population for recruiting volunteers. A real acquisition on the contents of a Spanish historical handwritten book was obtained with the platform. This data was used to perform experiments on the behaviour of the proposed framework. Some experiments were performed to study how to optimise the collaborators effort in terms of number of collaborations, including how many lines and which lines should be selected for the speech dictation.This work was supported in part by projects READ-674943 (European Union's H2020), SmartWays-RTC-2014-1466-4 (MINECO), CoMUN-HaT-TIN2015-70924-C2-1-R (MINECO/FEDER), and ALMAMATER-PROMETEOII/2014/030 (Generalitat Valenciana).Granell Romero, E.; Martínez Hinarejos, CD. (2017). Multimodal Crowdsourcing for Transcribing Handwritten Documents. IEEE/ACM Transactions on Audio, Speech and Language Processing. 25(2):409-419. https://doi.org/10.1109/TASLP.2016.2634123S40941925

Multimodality, interactivity, and crowdsourcing for document transcription

This is the peer reviewed version of the following article: Granell, Emilio, Romero, Verónica, Martínez-Hinarejos, Carlos-D.. (2018). Multimodality, interactivity, and crowdsourcing for document transcription.Computational Intelligence, 34, 2, 398-419. DOI: 10.1111/coin.12169, which has been published in final form at http://doi.org/10.1111/coin.12169.. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Knowledge mining from documents usually use document engineering techniques that allow the user to access the information contained in documents of interest. In this framework, transcription may provide efficient access to the contents of handwritten documents. Manual transcription is a time-consuming task that can be sped up by using different mechanisms. A first possibility is employing state-of-the-art handwritten text recognition systems to obtain an initial draft transcription that can be manually amended. A second option is employing crowdsourcing to obtain a massive but not error-free draft transcription. In this case, when collaborators employ mobile devices, speech dictation can be used as a transcription source, and speech and handwritten text recognition can be fused to provide a better draft transcription, which can be amended with even less effort. A final option is using interactive assistive frameworks, where the automatic system that provides the draft transcription and the transcriber cooperate to generate the final transcription. The novel contributions presented in this work include the study of the data fusion on a multimodal crowdsourcing framework and its integration with an interactive system. The use of the proposed solutions reduces the required transcription effort and optimizes the overall performance and usability, allowing for a better transcription process.projects READ, Grant/Award Number: 674943; (European Union's H2020); Smart Ways, Grant/Award Number: RTC-2014-1466-4; (MINECO); CoMUN-HaT, Grant/Award Number: TIN2015-70924-C2-1-R; (MINECO / FEDER)Granell, E.; Romero, V.; Martínez-Hinarejos, C. (2018). Multimodality, interactivity, and crowdsourcing for document transcription. Computational Intelligence. 34(2):398-419. https://doi.org/10.1111/coin.12169S39841934

Image speech combination for interactive computer assisted transcription of handwritten documents

[EN] Handwritten document transcription aims to obtain the contents of a document to provide efficient information access to, among other, digitised historical documents. The increasing number of historical documents published by libraries and archives makes this an important task. In this context, the use of image processing and understanding techniques in conjunction with assistive technologies reduces the time and human effort required for obtaining the final perfect transcription. The assistive transcription system proposes a hypothesis, usually derived from a recognition process of the handwritten text image. Then, the professional transcriber feedback can be used to obtain an improved hypothesis and speed-up the final transcription. In this framework, a speech signal corresponding to the dictation of the handwritten text can be used as an additional source of information. This multimodal approach, that combines the image of the handwritten text with the speech of the dictation of its contents, could make better the hypotheses (initial and improved) offered to the transcriber. In this paper we study the feasibility of a multimodal interactive transcription system for an assistive paradigm known as Computer Assisted Transcription of Text Images. Different techniques are tested for obtaining the multimodal combination in this framework. The use of the proposed multimodal approach reveals a significant reduction of transcription effort with some multimodal combination techniques, allowing for a faster transcription process.Work partially supported by projects READ-674943 (European Union's H2020), SmartWays-RTC-2014-1466-4 (MINECO, Spain), and CoMUN-HaT-TIN2015-70924-C2-1-R (MINECO/FEDER), and by Generalitat Valenciana (GVA), Spain under reference PROMETEOII/2014/030.Granell, E.; Romero, V.; Martínez-Hinarejos, C. (2019). Image speech combination for interactive computer assisted transcription of handwritten documents. Computer Vision and Image Understanding. 180:74-83. https://doi.org/10.1016/j.cviu.2019.01.009S748318

Smart Collaborative Mobile System for Taking Care of Disabled and Elderly People

Official statistics data show that in many countries the population is aging. In addition, there are several illnesses and disabilities that also affect a small sector of the population. In recent years, researchers and medical foundations are working in order to develop systems based on new technologies and enhance the quality of life of them. One of the cheapest ways is to take advantage of the features provided by the smartphones. Nowadays, the development of reduced size smartphones, but with high processing capacity, has increased dramatically. We can take profit of the sensors placed in smartphones in order to monitor disabled and elderly people. In this paper, we propose a smart collaborative system based on the sensors embedded in mobile devices, which permit us to monitor the status of a person based on what is happening in the environment, but comparing and taking decisions based on what is happening to its neighbors. The proposed protocol for the mobile ad hoc network and the smart system algorithm are described in detail. We provide some measurements showing the decisions taken for several common cases and we also show the performance of our proposal when there is a medium size group of disabled or elderly people. Our proposal can also be applied to take care of children in several situations.This work has been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, and by National Funding from the FCT - Fundacao para a Ciencia e a Tecnologia through the PEst-OE/EEI/LA0008/2011 Project.Sendra Compte, S.; Granell Romero, E.; Lloret, J.; Rodrigues, JJPC. (2014). Smart Collaborative Mobile System for Taking Care of Disabled and Elderly People. 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IEEE 802.11g Radio Coverage Study for Indoor Wireless Network Redesign

An efficient wireless design and development is essential to ensure a good performance of the WLANs. It supposes a good estimation of the number of APs, their locations according to the structure of the building, a good channel distribution and an adequate level of transmission power in order to avoid overlapping but providing the largest coverage. Otherwise, a WLAN may be composed by more access points, so it may be more expensive, but with a worse function due to the radio overlapping among APs in the same channel. In this paper, we show how a WLAN can be redesigned in order to improve its wireless coverage and function. It is based on studying the distribution and features of a public building in a Spanish University in order to determine the optimum access point location and to assign the appropriated channel. In this case, this WLAN allows users to connect to one of the available SSIDs in the target building. Results obtained from the proposed redesign have been very successful from the point of view of performance and coverage.Sendra, S.; Bri Molinero, D.; Granell Romero, E.; Lloret, J. (2012). IEEE 802.11g Radio Coverage Study for Indoor Wireless Network Redesign. International Journal on Advances in Intelligent Systems. 5(4):518-532. http://hdl.handle.net/10251/47031S5185325

Plant production of Cistus heterophyllus carthaginensis, species catalogued as Endangered in the Valencian Community (Spain)

Cistus heterophyllus subsp. carthaginensis (Cistaceae) es una especie catalogada como “En peligro de extinción” en la Comunidad Valenciana (España), representada por un único individuo silvestre, localizado en La Pobla de Vallbona (Valencia). Es una especie endémica de la Península Ibérica (Murcia y Valencia), considerando a menudo al ejemplar valenciano como el único individuo puro que existe en todo el mundo para este taxon, ya que el resto de poblaciones, halladas en Cartagena (Murcia), provienen de la hibridación con Cistus albidus. Recientemente, se ha publicado el Plan de recuperación para esta especie en la Comunidad Valenciana, que incluye la propagación y producción de nuevas plantas para la conservación a través de translocaciones, entre otras muchas acciones. Desde el punto de vista biológico, se trata de una especie auto-incompatible (aunque en ocasiones se ha registrado una pequeña producción de semillas viables), por lo que su propagación se convierte en un proceso complejo. Además, el uso alternativo de la multiplicación clonal in vitro es desaconsejable, debido a los cambios genéticos encontrados en las plantas producidas mediante esta técnica. En el Centro para la Investigación y Experimentación Forestal (CIEF-Servicio de Vida Silvestre) de la Generalitat Valenciana se han llevado a cabo trabajos experimentales de polinización manual para el cultivo y producción de semillas, así como para la multiplicación vegetativa por esquejes, lo que ha dado como resultado nuevos materiales de reproducción (semillas y plantas) de gran importancia para la conservación de esta especie.Cistus heterophyllus subsp. carthaginensis (Cistaceae) is an endangered species in the Valencian Community (Spain), represented by only one wild individual located in La Pobla de Vallbona (province of Valencia). It is an endemic species to the Iberian Peninsula (Murcia and Valencia), and the Valencian plant is often considered as the unique pure individual found worldwide. The remainder populations, found in Cartagena (Murcia) are thought to come from hybridization. Recently a recovery plan for this species has been approved, which includes the propagation and the production of new plants for conservation translocations, among other recovery actions. It deals with a self-incompatible species (although a small production of viable seeds has been recorded sometimes), so its propagation becomes a complex process. In addition, alternative use of in vitro propagation is inadvisable, due to apparent genetic changes found in the clones. The Centre for Forestry Research and Experimentation (CIEF, Valencian Wildlife Service) of the Generalitat Valenciana, has carried out experimental hand-pollination works for breeding and seed production, as well as vegetative propagation multiplication by cuttings that have successfully resulted in new reproductive material (seeds and plants) able to save this endangered species

The Percepción Smart Campus system

Ponènica presentada a IberSPEECH 2014, VIII Jornadas en Tecnología del Habla and IV Iberian SLTech Workshop, celebrat a Las Palmas de Gran Canaria els dies 19-21 de novembre de 2014This paper presents the capabilities of the Smart Campus system developed during the Percepcion project. The Smart Campus system is able to locate the user of the application in a limited environment, including indoor location. The system is able to show routes and data (using virtual reality) on the different elements of the environment. Speech queries could be used to locate places and get routes and information on that places

Conservación y distribución de las accesiones del Banco de Germoplasma de la Flora Silvestre Valenciana en la colección CIEF

The Germplasm Bank of Valencia Wild Flora is a network of collections for conservation of taxa listed in Valencia Catalog Endangered Species of Flora (VCESF). CIEF germplasm collection belongs to Generalitat Valenciana´s Forest Research and Experimentation Centre, and in addition to forest species, seeds of singular plants of VCESF are also preserved by the Wildlife Service team. From 1990 until now, in bank´s installations is preserved 2447 lots from 1445 species. It is conserved germplasm from 92% of the Endangered species and 94% as Vulnerable species according to VCESF. These data indicate that 67,6% of monitoring populations currently known for species listed as Endangered are preserved in the collection and 50% in the case of Vulnerable species. High-density areas of harvesting are the north and northwest of Alicante (dianic area and “Sierra de Aitana”), the southeast quadrant of Valencia (setabense zone), the Valencian coast (“El Saler”, “Sagunto”, etc.), and Castellón (“Islas Columbretes”, “Macizo del Penyagolosa”, “Cabanes”, “Peñiscola”). The aim to short-medium term is to keep up at least one accession of each monitoring population from VCESF species capable to be conserved in a genebank

Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

[EN] The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV) words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs). They show that sub-lexical units outperform word units in terms of Word Error Rate (WER), Character Error Rate (CER) and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs) and Convolutional Recurrent Neural Nets (CRNNs). Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition.Work partially supported by projects READ: Recognition and Enrichment of Archival Documents - 674943 (European Union's H2020) and CoMUN-HaT: Context, Multimodality and User Collaboration in Handwritten Text Processing - TIN2015-70924-C2-1-R (MINECO/FEDER), and a DGA-MRIS (Direction Generale de l'Armement - Mission pour la Recherche et l'Innovation Scientifique) scholarship.Granell, E.; Chammas, E.; Likforman-Sulem, L.; Martínez-Hinarejos, C.; Mokbel, C.; Cirstea, B. (2018). Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks. Journal of imaging. 4(1). https://doi.org/10.3390/jimaging4010015S154