Improving on-line handwritten recognition in interactive machine translation

[EN] On-line handwriting text recognition (HTR) could be used as a more natural way of interaction in many interactive applications. However, current HTR technology is far from developing error-free systems and, consequently, its use in many applications is limited. Despite this, there are many scenarios, as in the correction of the errors of fully-automatic systems using HTR in a post-editing step, in which the information from the specific task allows to constrain the search and therefore to improve the HTR accuracy. For example, in machine translation (MT), the on-line HTR system can also be used to correct translation errors. The HTR can take advantage of information from the translation problem such as the source sentence that is translated, the portion of the translated sentence that has been supervised by the human, or the translation error to be amended. Empirical experimentation suggests that this is a valuable information to improve the robustness of the on-line HTR system achieving remarkable results.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreement no. 287576 (CasMaCat), from the EC (FEDER/FSE), and from the Spanish MEC/MICINN under the Active2Trans (TIN2012-31723) project. It is also supported by the Generalitat Valenciana under Grant ALMPR (Prometeo/2009/01) and GV/2010/067.Alabau Gonzalvo, V.; Sanchis Navarro, JA.; Casacuberta Nolla, F. (2014). Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition. 47(3):1217-1228. https://doi.org/10.1016/j.patcog.2013.09.035S1217122847

Implementation of a Human-Computer Interface for Computer Assisted Translation and Handwritten Text Recognition

A human-computer interface is developed to provide services of computer assisted machine translation (CAT) and computer assisted transcription of handwritten text images (CATTI). The back-end machine translation (MT) and handwritten text recognition (HTR) systems are provided by the Pattern Recognition and Human Language Technology (PRHLT) research group. The idea is to provide users with easy to use tools to convert interactive translation and transcription feasible tasks. The assisted service is provided by remote servers with CAT or CATTI capabilities. The interface supplies the user with tools for efficient local edition: deletion, insertion and substitution.Ocampo Sepúlveda, JC. (2009). Implementation of a Human-Computer Interface for Computer Assisted Translation and Handwritten Text Recognition. http://hdl.handle.net/10251/14318Archivo delegad

Multiple Contributions to Interactive Transcription and Translation of Old Text Documents

There are huge historical document collections residing in libraries, museums and archives that are currently being digitized for preservation purposes and to make them available worldwide through large, on-line digital libraries. The main objective, however, is not to simply provide access to raw images of digitized documents, but to annotate them with their real informative content and, in particular, with text transcriptions and, if convenient, text translations too. This work aims at contributing to the development of advanced techniques and interfaces for the analysis, transcription and translation of images of old archive documents, following an interactive-predictive approach.Serrano Martínez-Santos, N. (2009). Multiple Contributions to Interactive Transcription and Translation of Old Text Documents. http://hdl.handle.net/10251/11272Archivo delegad

Segment-based interactive-predictive machine translation

[EN] Machine translation systems require human revision to obtain high-quality translations. Interactive methods provide an efficient human¿computer collaboration, notably increasing productivity. Recently, new interactive protocols have been proposed, seeking for a more effective user interaction with the system. In this work, we present one of these new protocols, which allows the user to validate all correct word sequences in a translation hypothesis. Thus, the left-to-right barrier from most of the existing protocols is broken. We compare this protocol against the classical prefix-based approach, obtaining a significant reduction of the user effort in a simulated environment. Additionally, we experiment with the use of confidence measures to select the word the user should correct at each iteration, reaching the conclusion that the order in which words are corrected does not affect the overall effort.The research leading to these results has received funding from the Ministerio de Economia y Competitividad (MINECO) under Project CoMUN-HaT (Grant Agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana under Project ALMAMATER (Ggrant Agreement PROMETEOII/2014/030).Domingo-Ballester, M.; Peris-Abril, Á.; Casacuberta Nolla, F. (2017). Segment-based interactive-predictive machine translation. Machine Translation. 31(4):163-185. https://doi.org/10.1007/s10590-017-9213-3S163185314Alabau V, Bonk R, Buck C, Carl M, Casacuberta F, García-Martínez M, González-Rubio J, Koehn P, Leiva LA, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2013) CASMACAT: an open source workbench for advanced computer aided translation. Prague Bull Math Linguist 100:101–112Alabau V, Rodríguez-Ruiz L, Sanchis A, Martínez-Gómez P, Casacuberta F (2011) On multimodal interactive machine translation using speech recognition. In: Proceedings of the International Conference on Multimodal Interaction, pp 129–136Alabau V, Sanchis A, Casacuberta F (2014) Improving on-line handwritten recognition in interactive machine translation. Pattern Recognit 47(3):1217–1228Apostolico A, Guerra C (1987) The longest common subsequence problem revisited. Algorithmica 2:315–336Azadi F, Khadivi S (2015) Improved search strategy for interactive machine translation in computer assisted translation. In: Proceedings of Machine Translation Summit XV, pp 319–332Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the International Conference on Learning Representations. arXiv:1409.0473Barrachina S, Bender O, Casacuberta F, Civera J, Cubel E, Khadivi S, Lagarda A, Ney H, Tomás J, Vidal E, Vilar J-M (2009) Statistical approaches to computer-assisted translation. Comput Linguist 35:3–28Brown PF, Pietra VJD, Pietra SAD, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. Comput Linguist 19(2):263–311Chen SF, Goodman J (1996) An empirical study of smoothing techniques for language modeling. In: Proceedings of the Annual Meeting on Association for Computational Linguistics, pp 310–318Cheng S, Huang S, Chen H, Dai X, Chen J (2016) PRIMT: a pick-revise framework for interactive machine translation. In: Proceedings of the North American Chapter of the Association for Computational Linguistics, pp 1240–1249Dale R (2016) How to make money in the translation business. Nat Lang Eng 22(2):321–325Domingo M, Peris, Á, Casacuberta F (2016) Interactive-predictive translation based on multiple word-segments. In: Proceedings of the Annual Conference of the European Association for Machine Translation, pp 282–291Federico M, Bentivogli L, Paul M, Stüker S (2011) Overview of the IWSLT 2011 evaluation campaign. In: International Workshop on Spoken Language Translation, pp 11–27Foster G, Isabelle P, Plamondon P (1997) Target-text mediated interactive machine translation. Mach Transl 12:175–194González-Rubio J, Benedí J-M, Casacuberta F (2016) Beyond prefix-based interactive translation prediction. In: Proceedings of the SIGNLL Conference on Computational Natural Language Learning, pp 198–207González-Rubio J, Ortiz-Martínez D, Casacuberta F (2010) On the use of confidence measures within an interactive-predictive machine translation system. In: Proceedings of the Annual Conference of the European Association for Machine TranslationKnowles R, Koehn P (2016) Neural interactive translation prediction. In: Proceedings of the Association for Machine Translation in the Americas, pp 107–120Koehn P (2005) Europarl: a parallel corpus for statistical machine translation. In: Proceedings of the Machine Translation Summit, pp 79–86Koehn P (2010) Statistical machine translation. Cambridge University Press, CambridgeKoehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp 177–180Koehn P, Och FJ, Marcu D (2003) Statistical phrase-based translation. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp 48–54Koehn P, Tsoukala C, Saint-Amand H (2014) Refinements to interactive translation prediction based on search graphs. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp 574–578Marie B, Max A (2015) Touch-based pre-post-editing of machine translation output. In: Proceedings of the conference on empirical methods in natural language processing, pp 1040–1045Nepveu L, Lapalme G, Langlais P, Foster G (2004) Adaptive language and translation models for interactive machine translation. In: Proceedings of the conference on empirical method in natural language processing, pp 190–197Nielsen J (1993) Usability engineering. Morgan Kaufmann Publishers Inc, BurlingtonOch F J (2003) Minimum error rate training in statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 160–167Och FJ, Ney H (2002) Discriminative training and maximum entropy models for statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 295–302Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Comput Linguist 29(1):19–51Ortiz-Martínez D (2016) Online learning for statistical machine translation. Comput Linguist 42(1):121–161Papineni K, Roukos S, Ward T, Zhu W-J (2002) BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 311–318Peris Á, Domingo M, Casacuberta F (2017) Interactive neural machine translation. Comput Speech Lang. 45:201–220Sanchis-Trilles G, Ortiz-Martínez D, Civera J, Casacuberta F, Vidal E, Hoang H (2008) Improving interactive machine translation via mouse actions. In: Proceedings of the conference on empirical methods in natural language processing, pp 485–494Snover M, Dorr B, Schwartz R, Micciulla L, Makhoul J (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the Association for Machine Translation in the Americas, pp 223–231Stolcke A (2002) SRILM—an extensible language modeling toolkit. In: Proceedings of the international conference on spoken language processing, pp 257–286Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. NIPS 27:3104–3112Tiedemann J (2009) News from OPUS—a collection of multilingual parallel corpora with tools and interfaces. Recent Adv Nat Lang Process 5:237–248Tomás J, Casacuberta F(2006) Statistical phrase-based models for interactive computer-assisted translation. In: Proceedings of the international conference on computational linguistics/Association for Computational Linguistics, pp 835–841Torregrosa D, Forcada ML, Pérez-Ortiz JA (2014) An open-source web-based tool for resource-agnostic interactive translation prediction. Prague Bull Math Linguist 102:69–80Tseng H, Chang P, Andrew G, Jurafsky D, Manning C (2005) A conditional random field word segmenter. In: Proceedings of the special interest group of the association for computational linguistics workshop on Chinese language processing, pp 168–171Ueffing N, Ney H (2005) Application of word-level confidence measures in interactive statistical machine translation. In: Proceedings of the European Association for Machine Translation, pp 262–270Vogel S, Ney H, Tillmann C (1996) HMM-based word alignment in statistical translation. Proc Conf Comput Linguist 2:836–841Wuebker J, Green S, DeNero J, Hasan S, Luong M-T(2016) Models and inference for prefix-constrained machine translation. In: Proceedings of the annual meeting of the association for the computational linguistics, pp 66–75Zens R, Och FJ, Ney H (2002) Phrase-based statistical machine translation. In: Proceedings of the annual German conference on advances in artificial intelligence 2479:18–3

Contributions to Pen & Touch Human-Computer Interaction

[EN] Computers are now present everywhere, but their potential is not fully exploited due to some lack of acceptance. In this thesis, the pen computer paradigm is adopted, whose main idea is to replace all input devices by a pen and/or the fingers, given that the origin of the rejection comes from using unfriendly interaction devices that must be replaced by something easier for the user. This paradigm, that was was proposed several years ago, has been only recently fully implemented in products, such as the smartphones. But computers are actual illiterates that do not understand gestures or handwriting, thus a recognition step is required to "translate" the meaning of these interactions to computer-understandable language. And for this input modality to be actually usable, its recognition accuracy must be high enough. In order to realistically think about the broader deployment of pen computing, it is necessary to improve the accuracy of handwriting and gesture recognizers. This thesis is devoted to study different approaches to improve the recognition accuracy of those systems. First, we will investigate how to take advantage of interaction-derived information to improve the accuracy of the recognizer. In particular, we will focus on interactive transcription of text images. Here the system initially proposes an automatic transcript. If necessary, the user can make some corrections, implicitly validating a correct part of the transcript. Then the system must take into account this validated prefix to suggest a suitable new hypothesis. Given that in such application the user is constantly interacting with the system, it makes sense to adapt this interactive application to be used on a pen computer. User corrections will be provided by means of pen-strokes and therefore it is necessary to introduce a recognizer in charge of decoding this king of nondeterministic user feedback. However, this recognizer performance can be boosted by taking advantage of interaction-derived information, such as the user-validated prefix. Then, this thesis focuses on the study of human movements, in particular, hand movements, from a generation point of view by tapping into the kinematic theory of rapid human movements and the Sigma-Lognormal model. Understanding how the human body generates movements and, particularly understand the origin of the human movement variability, is important in the development of a recognition system. The contribution of this thesis to this topic is important, since a new technique (which improves the previous results) to extract the Sigma-lognormal model parameters is presented. Closely related to the previous work, this thesis study the benefits of using synthetic data as training. The easiest way to train a recognizer is to provide "infinite" data, representing all possible variations. In general, the more the training data, the smaller the error. But usually it is not possible to infinitely increase the size of a training set. Recruiting participants, data collection, labeling, etc., necessary for achieving this goal can be time-consuming and expensive. One way to overcome this problem is to create and use synthetically generated data that looks like the human. We study how to create these synthetic data and explore different approaches on how to use them, both for handwriting and gesture recognition. The different contributions of this thesis have obtained good results, producing several publications in international conferences and journals. Finally, three applications related to the work of this thesis are presented. First, we created Escritorie, a digital desk prototype based on the pen computer paradigm for transcribing handwritten text images. Second, we developed "Gestures à Go Go", a web application for bootstrapping gestures. Finally, we studied another interactive application under the pen computer paradigm. In this case, we study how translation reviewing can be done more ergonomically using a pen.[ES] Hoy en día, los ordenadores están presentes en todas partes pero su potencial no se aprovecha debido al "miedo" que se les tiene. En esta tesis se adopta el paradigma del pen computer, cuya idea fundamental es sustituir todos los dispositivos de entrada por un lápiz electrónico o, directamente, por los dedos. El origen del rechazo a los ordenadores proviene del uso de interfaces poco amigables para el humano. El origen de este paradigma data de hace más de 40 años, pero solo recientemente se ha comenzado a implementar en dispositivos móviles. La lenta y tardía implantación probablemente se deba a que es necesario incluir un reconocedor que "traduzca" los trazos del usuario (texto manuscrito o gestos) a algo entendible por el ordenador. Para pensar de forma realista en la implantación del pen computer, es necesario mejorar la precisión del reconocimiento de texto y gestos. El objetivo de esta tesis es el estudio de diferentes estrategias para mejorar esta precisión. En primer lugar, esta tesis investiga como aprovechar información derivada de la interacción para mejorar el reconocimiento, en concreto, en la transcripción interactiva de imágenes con texto manuscrito. En la transcripción interactiva, el sistema y el usuario trabajan "codo con codo" para generar la transcripción. El usuario valida la salida del sistema proporcionando ciertas correcciones, mediante texto manuscrito, que el sistema debe tener en cuenta para proporcionar una mejor transcripción. Este texto manuscrito debe ser reconocido para ser utilizado. En esta tesis se propone aprovechar información contextual, como por ejemplo, el prefijo validado por el usuario, para mejorar la calidad del reconocimiento de la interacción. Tras esto, la tesis se centra en el estudio del movimiento humano, en particular del movimiento de las manos, utilizando la Teoría Cinemática y su modelo Sigma-Lognormal. Entender como se mueven las manos al escribir, y en particular, entender el origen de la variabilidad de la escritura, es importante para el desarrollo de un sistema de reconocimiento, La contribución de esta tesis a este tópico es importante, dado que se presenta una nueva técnica (que mejora los resultados previos) para extraer el modelo Sigma-Lognormal de trazos manuscritos. De forma muy relacionada con el trabajo anterior, se estudia el beneficio de utilizar datos sintéticos como entrenamiento. La forma más fácil de entrenar un reconocedor es proporcionar un conjunto de datos "infinito" que representen todas las posibles variaciones. En general, cuanto más datos de entrenamiento, menor será el error del reconocedor. No obstante, muchas veces no es posible proporcionar más datos, o hacerlo es muy caro. Por ello, se ha estudiado como crear y usar datos sintéticos que se parezcan a los reales. Las diferentes contribuciones de esta tesis han obtenido buenos resultados, produciendo varias publicaciones en conferencias internacionales y revistas. Finalmente, también se han explorado tres aplicaciones relaciones con el trabajo de esta tesis. En primer lugar, se ha creado Escritorie, un prototipo de mesa digital basada en el paradigma del pen computer para realizar transcripción interactiva de documentos manuscritos. En segundo lugar, se ha desarrollado "Gestures à Go Go", una aplicación web para generar datos sintéticos y empaquetarlos con un reconocedor de forma rápida y sencilla. Por último, se presenta un sistema interactivo real bajo el paradigma del pen computer. En este caso, se estudia como la revisión de traducciones automáticas se puede realizar de forma más ergonómica.[CA] Avui en dia, els ordinadors són presents a tot arreu i es comunament acceptat que la seva utilització proporciona beneficis. No obstant això, moltes vegades el seu potencial no s'aprofita totalment. En aquesta tesi s'adopta el paradigma del pen computer, on la idea fonamental és substituir tots els dispositius d'entrada per un llapis electrònic, o, directament, pels dits. Aquest paradigma postula que l'origen del rebuig als ordinadors prové de l'ús d'interfícies poc amigables per a l'humà, que han de ser substituïdes per alguna cosa més coneguda. Per tant, la interacció amb l'ordinador sota aquest paradigma es realitza per mitjà de text manuscrit i/o gestos. L'origen d'aquest paradigma data de fa més de 40 anys, però només recentment s'ha començat a implementar en dispositius mòbils. La lenta i tardana implantació probablement es degui al fet que és necessari incloure un reconeixedor que "tradueixi" els traços de l'usuari (text manuscrit o gestos) a alguna cosa comprensible per l'ordinador, i el resultat d'aquest reconeixement, actualment, és lluny de ser òptim. Per pensar de forma realista en la implantació del pen computer, cal millorar la precisió del reconeixement de text i gestos. L'objectiu d'aquesta tesi és l'estudi de diferents estratègies per millorar aquesta precisió. En primer lloc, aquesta tesi investiga com aprofitar informació derivada de la interacció per millorar el reconeixement, en concret, en la transcripció interactiva d'imatges amb text manuscrit. En la transcripció interactiva, el sistema i l'usuari treballen "braç a braç" per generar la transcripció. L'usuari valida la sortida del sistema donant certes correccions, que el sistema ha d'usar per millorar la transcripció. En aquesta tesi es proposa utilitzar correccions manuscrites, que el sistema ha de reconèixer primer. La qualitat del reconeixement d'aquesta interacció és millorada, tenint en compte informació contextual, com per exemple, el prefix validat per l'usuari. Després d'això, la tesi se centra en l'estudi del moviment humà en particular del moviment de les mans, des del punt de vista generatiu, utilitzant la Teoria Cinemàtica i el model Sigma-Lognormal. Entendre com es mouen les mans en escriure és important per al desenvolupament d'un sistema de reconeixement, en particular, per entendre l'origen de la variabilitat de l'escriptura. La contribució d'aquesta tesi a aquest tòpic és important, atès que es presenta una nova tècnica (que millora els resultats previs) per extreure el model Sigma- Lognormal de traços manuscrits. De forma molt relacionada amb el treball anterior, s'estudia el benefici d'utilitzar dades sintètiques per a l'entrenament. La forma més fàcil d'entrenar un reconeixedor és proporcionar un conjunt de dades "infinit" que representin totes les possibles variacions. En general, com més dades d'entrenament, menor serà l'error del reconeixedor. No obstant això, moltes vegades no és possible proporcionar més dades, o fer-ho és molt car. Per això, s'ha estudiat com crear i utilitzar dades sintètiques que s'assemblin a les reals. Les diferents contribucions d'aquesta tesi han obtingut bons resultats, produint diverses publicacions en conferències internacionals i revistes. Finalment, també s'han explorat tres aplicacions relacionades amb el treball d'aquesta tesi. En primer lloc, s'ha creat Escritorie, un prototip de taula digital basada en el paradigma del pen computer per realitzar transcripció interactiva de documents manuscrits. En segon lloc, s'ha desenvolupat "Gestures à Go Go", una aplicació web per a generar dades sintètiques i empaquetar-les amb un reconeixedor de forma ràpida i senzilla. Finalment, es presenta un altre sistema inter- actiu sota el paradigma del pen computer. En aquest cas, s'estudia com la revisió de traduccions automàtiques es pot realitzar de forma més ergonòmica.Martín-Albo Simón, D. (2016). Contributions to Pen & Touch Human-Computer Interaction [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/68482TESI

Interactive Transcription of Old Text Documents

Nowadays, there are huge collections of handwritten text documents in libraries all over the world. The high demand for these resources has led to the creation of digital libraries in order to facilitate the preservation and provide electronic access to these documents. However text transcription of these documents im- ages are not always available to allow users to quickly search information, or computers to process the information, search patterns or draw out statistics. The problem is that manual transcription of these documents is an expensive task from both economical and time viewpoints. This thesis presents a novel ap- proach for e cient Computer Assisted Transcription (CAT) of handwritten text documents using state-of-the-art Handwriting Text Recognition (HTR) systems. The objective of CAT approaches is to e ciently complete a transcription task through human-machine collaboration, as the e ort required to generate a manual transcription is high, and automatically generated transcriptions from state-of-the-art systems still do not reach the accuracy required. This thesis is centered on a special application of CAT, that is, the transcription of old text document when the quantity of user e ort available is limited, and thus, the entire document cannot be revised. In this approach, the objective is to generate the best possible transcription by means of the user e ort available. This thesis provides a comprehensive view of the CAT process from feature extraction to user interaction. First, a statistical approach to generalise interactive transcription is pro- posed. As its direct application is unfeasible, some assumptions are made to apply it to two di erent tasks. First, on the interactive transcription of hand- written text documents, and next, on the interactive detection of the document layout. Next, the digitisation and annotation process of two real old text documents is described. This process was carried out because of the scarcity of similar resources and the need of annotated data to thoroughly test all the developed tools and techniques in this thesis. These two documents were carefully selected to represent the general di culties that are encountered when dealing with HTR. Baseline results are presented on these two documents to settle down a benchmark with a standard HTR system. Finally, these annotated documents were made freely available to the community. It must be noted that, all the techniques and methods developed in this thesis have been assessed on these two real old text documents. Then, a CAT approach for HTR when user e ort is limited is studied and extensively tested. The ultimate goal of applying CAT is achieved by putting together three processes. Given a recognised transcription from an HTR system. The rst process consists in locating (possibly) incorrect words and employs the user e ort available to supervise them (if necessary). As most words are not expected to be supervised due to the limited user e ort available, only a few are selected to be revised. The system presents to the user a small subset of these words according to an estimation of their correctness, or to be more precise, according to their con dence level. Next, the second process starts once these low con dence words have been supervised. This process updates the recogni- tion of the document taking user corrections into consideration, which improves the quality of those words that were not revised by the user. Finally, the last process adapts the system from the partially revised (and possibly not perfect) transcription obtained so far. In this adaptation, the system intelligently selects the correct words of the transcription. As results, the adapted system will bet- ter recognise future transcriptions. Transcription experiments using this CAT approach show that this approach is mostly e ective when user e ort is low. The last contribution of this thesis is a method for balancing the nal tran- scription quality and the supervision e ort applied using our previously de- scribed CAT approach. In other words, this method allows the user to control the amount of errors in the transcriptions obtained from a CAT approach. The motivation of this method is to let users decide on the nal quality of the desired documents, as partially erroneous transcriptions can be su cient to convey the meaning, and the user e ort required to transcribe them might be signi cantly lower when compared to obtaining a totally manual transcription. Consequently, the system estimates the minimum user e ort required to reach the amount of error de ned by the user. Error estimation is performed by computing sepa- rately the error produced by each recognised word, and thus, asking the user to only revise the ones in which most errors occur. Additionally, an interactive prototype is presented, which integrates most of the interactive techniques presented in this thesis. This prototype has been developed to be used by palaeographic expert, who do not have any background in HTR technologies. After a slight ne tuning by a HTR expert, the prototype lets the transcribers to manually annotate the document or employ the CAT ap- proach presented. All automatic operations, such as recognition, are performed in background, detaching the transcriber from the details of the system. The prototype was assessed by an expert transcriber and showed to be adequate and e cient for its purpose. The prototype is freely available under a GNU Public Licence (GPL).Serrano Martínez-Santos, N. (2014). Interactive Transcription of Old Text Documents [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37979TESI

Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017