Suchergebnisse

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_24 ; [EN] The amount of digitized legacy documents has been rising over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents. The vast majority of them remain waiting to be transcribed to provide historians and other researchers new ways of indexing, consulting and querying them. However, the performance accuracy of state-of-the-art Handwritten Text Recognition techniques decreases dramatically when they are applied to these historical documents. This is mainly due to the typical paper degradation problems. Therefore, robust pre-processing techniques is an important step for helping further recognition steps. This paper proposes to take existing binarization techniques, in order to retain their advantages, and modify them in such a way that some of the original grayscale information is preserved and be considered by the subsequent recognizer. Results are reported with the publicly available ESPOSALLES database. ; 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 project (TIN2012-37475-C02-01). ; Villegas, M.; Romero Gómez, V.; Sánchez Peiró, JA. (2015). On the modification of binarization algorithms to retain grayscale information for handwritten text recognition. En Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. Springer International Publishing. 208-215. https://doi.org/10.1007/978-3-319-19390-8_24 ; S ; 208 ; 215 ; Drida, F.: Towards restoring historic documents degraded over time. In: Proceedings of 2nd IEEE International Conference on Document Image Analysis for Libraries (DIAL 2006), Lyon, France, pp. 350–357 (2006) ; Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: ...

© 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] Existing transcripts for historic manuscripts are a very valuable resource for training models useful for automatic recognition, aided transcription, and/or indexing of the remaining untranscribed parts of these collections. However, these existing transcripts generally exhibit two main problems which hinder their convenience: a) text of the transcripts is seldom aligned with manuscript lines, and b) text often deviate very significantly from what can be seen in the manuscript, either because writing style has been modernized or abbreviations have been expanded, or both. This work presents an analysis of these problems and discusses possible solutions for minimizing human effort needed to adapt existing transcripts in order to render them usable. Empirical results presented show the huge performance gain that can be obtained by adequately adapting the transcripts, thus motivating future development of the proposed solutions. ; We are very grateful to Carlos Lechner and Celio Hernández who helped in the creation of the ground truth of the Alcaraz dataset. This work has been partially supported by the European Union (EU) Horizon 2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943), EU project HIMANIS (JPICH programme, Spanish grant Ref: PCIN-2015-068) and MINECO/FEDER, UE under project TIN2015-70924-C2-1-R. ; Villegas, M.; Toselli, AH.; Romero Gómez, V.; Vidal, E. (2016). Exploiting Existing Modern Transcripts for Historical Handwritten Text Recognition. IEEE. https://doi.org/10.1109/ICFHR.2016.22 ; S

© 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] This paper describes the Handwritten Text Recognition (HTR) competition on the READ dataset that has been held in the context of the International Conference on Frontiers in Handwriting Recognition 2016. This competition aims to bring together researchers working on off-line HTR and provide them a suitable benchmark to compare their techniques on the task of transcribing typical historical handwritten documents. Two tracks with different conditions on the use of training data were proposed. Ten research groups registered in the competition but finally five submitted results. The handwritten images for this competition were drawn from the German document Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805, used in the READ project. The selected dataset is written by several hands and entails significant variabilities and difficulties. The five participants achieved good results with transcriptions word error rates ranging from 21% to 47% and character error rates rating from 5% to 19%. ; This work has been partially supported through the European Union's H2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943), and the MINECO/FEDER UE project TIN2015-70924-C2-1-R. ; Sánchez Peiró, JA.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2016). ICFHR2016 Competition on Handwritten Text Recognition on the READ Dataset. IEEE. https://doi.org/10.1109/ICFHR.2016.0120 ; S

© 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. ; Handwritten marriage licenses books have been used for centuries by ecclesiastical and secular institutions to register marriages. The information contained in these historical documents is useful for demography studies and genealogical research, among others. Despite the generally simple structure of the text in these documents, automatic transcription and semantic information extraction is difficult due to the distinct and evolutionary vocabulary, which is composed mainly of proper names that change along the time. In previous works we studied the use of category-based language models to both improve the automatic transcription accuracy and make easier the extraction of semantic information. Here we analyze the main causes of the semantic errors observed in previous results and apply a Grammatical Inference technique known as MGGI to improve the semantic accuracy of the language model obtained. Using this language model, full handwritten text recognition experiments have been carried out, with results supporting the interest of the proposed approach. ; This work has been partially supported through the European Union's H2020 grant READ (Ref: 674943), the European project ERC-2010-AdG-20100407-269796, the MINECO/FEDER, UE projects TIN2015-70924-C2-1-R and TIN2015-70924-C2-2-R, and the Ramon y Cajal Fellowship RYC-2014-16831. ; Romero Gómez, V.; Fornes, A.; Vidal Ruiz, E.; Sánchez Peiró, JA. (2016). Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books. IEEE. https://doi.org/10.1109/ICFHR.2016.0069 ; S

"The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-21257-4_85 ; To date, automatic handwriting text recognition systems are far from being perfect and heavy human intervention is often required to check and correct the results of such systems. As an alternative, an interactive framework that integrates the human knowledge into the transcription process has been presented in previous works. In this work, multimodal interaction at character-level is studied. Until now, multimodal interaction had been studied only at whole-word level. However, character-level pen-stroke interactions may lead to more ergonomic and friendly interfaces. Empirical tests show that this approach can save significant amounts of user effort with respect to both fully manual transcription and non-interactive post-editing correction. ; Work supported by the Spanish Government (MICINN and "Plan E") under the MITTRAL (TIN2009-14633-C03-01) research project and under the research programme Consolider Ingenio 2010: MIPRCV (CSD2007-00018), and by the Generalitat Valenciana under grant Prometeo/2009/014. ; Martín-Albo Simón, D.; Romero Gómez, V.; Toselli ., AH.; Vidal, E. (2011). Character-level interaction in multimodal computer-assisted transcription of text images. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 684-691. https://doi.org/10.1007/978-3-642-21257-4 ; S ; 684 ; 691

©2014 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. ; A contest on Handwritten Text Recognition organised in the context of the ICFHR 2014 conference is described. Two tracks with increased freedom on the use of training data were proposed and three research groups participated in these two tracks. The handwritten images for this contest were drawn from an English data set which is currently being considered in the tranScriptorium project. The the goal of this project is to develop innovative, efficient and cost-effective solutions for the transcription of historical handwritten document images, focusing on four languages: English, Spanish, German and Dutch. For the English language, the so-called "Bentham collection" is being considered in tranScriptorium. It encompasses a large set of manuscripts written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). A small subset of this collection has been chosen for the present HTR competition. The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant varibilities and difficulties regarding the quality of text images and writting styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts. The three participants achieved very good results, with transcription word error rates ranging from 15.0% down to 8.6%. ; The research leading to these results has received funding from the European Union's Seventh Framework Pro-gramme (FP7/2007-2013) under grant agreement no. 600707- tranScriptorium. The authors would like to thank all theTRANSCRIPTORIUMmembers for their collaboration and the entrants ...

""© 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/10.1145/2595188.2595193 ; Transcription 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 ; S

© 2014 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. ; Tran Scriptorium is a 3-years project that aims to develop innovative, cost-effective solutions for the indexing, search and full transcription of historical handwritten document images, using Handwritten Text Recognition (HTR) technology. The production of ground-truth (GT) of a dataset of handwritten document images is among the first tasks. We address novel approaches for the faster production of this GT based on crowd-sourcing and on prior-knowledge methods. We also address here a novel low-cost semi-supervised procedure for obtaining pairs of correct line-level aligned detected/extracted text line images and text line transcripts, specially suitable for training models of the HTR technology employed in Tran Scriptorium. ; Work supported by the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement No.600707 - tranScriptorium. ; Gatos, B.; Louloudis, G.; Caser, T.; Grint, K.; Romero Gómez, V.; Sánchez Peiró, JA.; Toselli, AH. (2014). Ground-Truth production in the tranScriptorium Project. En Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on. IEEE Computer Society - Conference Publishing Services (CPS). 237-241. https://doi.org/10.1109/DAS.2014.23 ; S ; 237 ; 241