Contributions to Adaptation on Automatic Speech Recognition and Multilingual Handwritten Text Recognition

[ES En este trabajo se han estudiado métodos para adaptación en la transcripción automática de documentos manuscritos multilíngües y además se han propuesto diversas técnicas para identificación del idioma. Por otra parte, en cuanto a la adaptación en el reconocimiento del habla, se aplica una técnica conocida de adaptación al locutor (MLLR). Por último, se presenta una aplicación en el marco del proyecto transLectures, cuyo objetivo es integrar en un reproductor multimedia un sistema de transcripción automática interactivo.[EN] In this work we have studied several methods for adaptation on automatic handwriting recognition of multilingual documents, so as it has been propossed different language identification techniques. Regarding adaptation in speech recognition, it has been applied a well-known technique for speaker adaptation (MLLR). Finally, we present an application under the transLectures project, which is a real application example of an interactive automatic transcription system within a video player.Del Agua Teba, MA. (2012). Contributions to Adaptation on Automatic Speech Recognition and Multilingual Handwritten Text Recognition. http://hdl.handle.net/10251/19115Archivo delegad

Language model adaptation for video lectures transcription

Videolectures are currently being digitised all over the world for its enormous value as reference resource. Many of these lectures are accompanied with slides. The slides offer a great opportunity for improving ASR systems performance. We propose a simple yet powerful extension to the linear interpolation of language models for adapting language models with slide information. Two types of slides are considered, correct slides, and slides automatic extracted from the videos with OCR. Furthermore, we compare both time aligned and unaligned slides. Results report an improvement of up to 3.8 % absolute WER points when using correct slides. Surprisingly, when using automatic slides obtained with poor OCR quality, the ASR system still improves up to 2.2 absolute WER points.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755 (transLectures). Also supported by the Spanish Government (Plan E, iTrans2 TIN2009-14511).Martínez-Villaronga, A.; Del Agua Teba, MA.; Andrés Ferrer, J.; Juan Císcar, A. (2013). Language model adaptation for video lectures transcription. En Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IInstitute of Electrical and Electronics Engineers (IEEE). 8450-8454. https://doi.org/10.1109/ICASSP.2013.6639314S8450845

Language identification for interactive handwriting transcription of multilingual documents

An effective approach to handwriting transcription of (old) documents is to follow a sequential, line-by-line transcription of the whole document, in which a continuously retrained system interacts with the user. In the case of multilingual documents, however, a minor yet important issue for this interactive approach is to first identify the language of the current text line image to be transcribed. In this paper, we propose a probabilistic framework and three techniques for this purpose. Empirical results are reported on an entire 764-page multilingual document for which previous empirical tests were limited to its first 180 pages, written only in Spanish. © 2011 Springer-Verlag.Work supported by the EC (FEDER, FSE), the Spanish Government (MICINN, MITyC, “Plan E”, under grants MIPRCV “Consolider Ingenio 2010”, MITTRAL TIN2009- 14633-C03-01 and FPU AP2007-02867), the Generalitat Valenciana (grant Prometeo/2009/014 and ACOMP/2010/051) and the UPV (grant 20080033).Del Agua Teba, MA.; Serrano Martinez Santos, N.; Juan Císcar, A. (2011). Language identification for interactive handwriting transcription of multilingual documents. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 6669:596-603. https://doi.org/10.1007/978-3-642-21257-4_74S5966036669del Agua, M.A.: Multilingualidad en el reconocimiento de texto manuscrito. Final Degree Project (2010)Ghosh, D., Dube, T., Shivaprasad, P.: Script Recognition: A Review. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 32(12), 2142–2161 (2010)Pérez, D., Tarazón, L., Serrano, N., Castro, F., Ramos-Terrades, O., Juan, A.: The GERMANA database. In: Proc. of the 10th Int. Conf. on Document Analysis and Recognition (ICDAR 2009), Barcelona, Spain, pp. 301–305 (2009)Plötz, T., Fink, G.: Markov models for offline handwriting recognition: a survey. Int. J. on Document Analysis and Recognition (IJDAR) 12(4), 269–298 (2009)Serrano, N., Giménez, A., Sanchis, A., Juan, A.: Active learning strategies in handwritten text recognition. In: Proc. of the 12th Int. Conf. on Multimodal Interfaces and the 7th Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2010), Beijing (China), vol. 86 (November 2010)Serrano, N., Pérez, D., Sanchis, A., Juan, A.: Adaptation from Partially Supervised Handwritten Text Transcriptions. In: Proc. of the 11th Int. Conf. on Multimodal Interfaces and the 6th Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2009), Cambridge, MA (USA), pp. 289–292 (2009)Serrano, N., Sanchis, A., Juan, A.: Balancing error and supervision effort in interactive-predictive handwriting recognition. In: Proc. of the Int. Conf. on Intelligent User Interfaces (IUI 2010), Hong Kong (China), pp. 373–376 (2010)Serrano, N., Tarazón, L., Pérez, D., Ramos-Terrades, O., Juan, A.: The GIDOC prototype. In: Proc. of the 10th Int. Workshop on Pattern Recognition in Information Systems (PRIS 2010), Funchal (Portugal), pp. 82–89 (2010

Character-Based Handwritten Text Recognition of Multilingual Documents

[EN] An effective approach to transcribe handwritten text documents is to follow a sequential interactive approach. During the supervision phase, user corrections are incorporated into the system through an ongoing retraining process. In the case of multilingual documents with a high percentage of out-of-vocabulary (OOV) words, two principal issues arise. On the one hand, a minor yet important matter for this interactive approach is to identify the language of the current text line image to be transcribed, as a language dependent recognisers typically performs better than a monolingual recogniser. On the other hand, word-based language models suffer from data scarcity in the presence of a large number of OOV words, degrading their estimation and affecting the performance of the transcription system. In this paper, we successfully tackle both issues deploying character-based language models combined with language identification techniques on an entire 764-page multilingual document. The results obtained significantly reduce previously reported results in terms of transcription error on the same task, but showed that a language dependent approach is not effective on top of character-based recognition of similar languages.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n◦ 287755. Also supported by the Spanish Government (MIPRCV ”Consolider Ingenio 2010”, iTrans2 TIN2009-14511, MITTRAL TIN2009-14633-C03-01 and FPU AP2007-0286) and the Generalitat Valenciana (Prometeo/2009/014).Del Agua Teba, MA.; Serrano Martinez Santos, N.; Civera Saiz, J.; Juan Císcar, A. (2012). Character-Based Handwritten Text Recognition of Multilingual Documents. Communications in Computer and Information Science. 328:187-196. https://doi.org/10.1007/978-3-642-35292-8_20S187196328Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(5), 855–868 (2009)Serrano, N., Tarazón, L., Pérez, D., Ramos-Terrades, O., Juan, A.: The GIDOC prototype. In: Proc. of the 10th Int. Workshop on Pattern Recognition in Information Systems (PRIS 2010), Funchal, Portugal, pp. 82–89 (2010)Serrano, N., Pérez, D., Sanchis, A., Juan, A.: Adaptation from Partially Supervised Handwritten Text Transcriptions. In: Proc. of the 11th Int. Conf. on Multimodal Interfaces and the 6th Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2009), Cambridge, MA, USA, pp. 289–292 (2009)Serrano, N., Sanchis, A., Juan, A.: Balancing error and supervision effort in interactive-predictive handwriting recognition. In: Proc. of the Int. Conf. on Intelligent User Interfaces (IUI 2010), Hong Kong, China, pp. 373–376 (2010)Serrano, N., Giménez, A., Sanchis, A., Juan, A.: Active learning strategies in handwritten text recognition. In: Proc. of the 12th Int. Conf. on Multimodal Interfaces and the 7th Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2010), Beijing, China, vol. (86) (November 2010)Pérez, D., Tarazón, L., Serrano, N., Castro, F., Ramos-Terrades, O., Juan, A.: The GERMANA database. In: Proc. of the 10th Int. Conf. on Document Analysis and Recognition (ICDAR 2009), Barcelona, Spain, pp. 301–305 (2009)del Agua, M.A., Serrano, N., Juan, A.: Language Identification for Interactive Handwriting Transcription of Multilingual Documents. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 596–603. Springer, Heidelberg (2011)Ghosh, D., Dube, T., Shivaprasad, P.: Script Recognition: A Review. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 32(12), 2142–2161 (2010)Bisani, M., Ney, H.: Open vocabulary speech recognition with flat hybrid models. In: Proc. of the European Conf. on Speech Communication and Technology, pp. 725–728 (2005)Szoke, I., Burget, L., Cernocky, J., Fapso, M.: Sub-word modeling of out of vocabulary words in spoken term detection. 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In: Proc. of ICSLP 2002, pp. 901–904 (September 2002)Rybach, D., Gollan, C., Heigold, G., Hoffmeister, B., Lööf, J., Schlüter, R., Ney, H.: The RWTH aachen university open source speech recognition system. In: Interspeech, Brighton, U.K., pp. 2111–2114 (September 2009)Efron, B., Tibshirani, R.J.: An Introduction to Bootstrap. Chapman & Hall/CRC (1994

Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks

© 2018 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng 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] In the last years, Deep Bidirectional Recurrent Neural Networks (DBRNN) and DBRNN with Long Short-Term Memory cells (DBLSTM) have outperformed the most accurate classifiers for confidence estimation in automatic speech recognition. At the same time, we have recently shown that speaker adaptation of confidence measures using DBLSTM yields significant improvements over non-adapted confidence measures. In accordance with these two recent contributions to the state of the art in confidence estimation, this paper presents a comprehensive study of speaker-adapted confidence measures using DBRNN and DBLSTM models. Firstly, we present new empirical evidences of the superiority of RNN-based confidence classifiers evaluated over a large speech corpus consisting of the English LibriSpeech and the Spanish poliMedia tasks. Secondly, we show new results on speaker-adapted confidence measures considering a multi-task framework in which RNN-based confidence classifiers trained with LibriSpeech are adapted to speakers of the TED-LIUM corpus. These experiments confirm that speaker-adapted confidence measures outperform their non-adapted counterparts. Lastly, we describe an unsupervised adaptation method of the acoustic DBLSTM model based on confidence measures which results in better automatic speech recognition performance.This work was supported in part by the European Union's Horizon 2020 research and innovation programme under Grant 761758 (X5gon), in part by the Seventh Framework Programme (FP7/2007-2013) under Grant 287755 (transLectures), in part by the ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme under Grant 621030 (EMMA), and in part by the Spanish Government's TIN2015-68326-R (MINECO/FEDER) research project MORE.Del Agua Teba, MA.; Giménez Pastor, A.; Sanchis Navarro, JA.; Civera Saiz, J.; Juan, A. (2018). Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks. IEEE/ACM Transactions on Audio Speech and Language Processing. 26(7):1198-1206. https://doi.org/10.1109/TASLP.2018.2819900S1198120626

Statistical text-to-speech synthesis of Spanish subtitles

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-13623-3_5Online multimedia repositories are growing rapidly. However, language barriers are often difficult to overcome for many of the current and potential users. In this paper we describe a TTS Spanish sys- tem and we apply it to the synthesis of transcribed and translated video lectures. A statistical parametric speech synthesis system, in which the acoustic mapping is performed with either HMM-based or DNN-based acoustic models, has been developed. To the best of our knowledge, this is the first time that a DNN-based TTS system has been implemented for the synthesis of Spanish. A comparative objective evaluation between both models has been carried out. Our results show that DNN-based systems can reconstruct speech waveforms more accurately.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755 (transLectures) and ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme (CIP) under grant agreement no 621030 (EMMA), and the Spanish MINECO Active2Trans (TIN2012-31723) research project.Piqueras Gozalbes, SR.; Del Agua Teba, MA.; Giménez Pastor, A.; Civera Saiz, J.; Juan Císcar, A. (2014). Statistical text-to-speech synthesis of Spanish subtitles. En Advances in Speech and Language Technologies for Iberian Languages: Second International Conference, IberSPEECH 2014, Las Palmas de Gran Canaria, Spain, November 19-21, 2014. Proceedings. 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Language model adaptation for lecture transcription by document retrieval

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-13623-3_14With the spread of MOOCs and video lecture repositories it is more important than ever to have accurate methods for automatically transcribing video lectures. In this work, we propose a simple yet effective language model adaptation technique based on document retrieval from the web. This technique is combined with slide adaptation, and compared against a strong baseline language model and a stronger slide-adapted baseline. These adaptation techniques are compared within two different acoustic models: a standard HMM model and the CD-DNN-HMM model. The proposed method obtains improvements on WER of up to 14% relative with respect to a competitive baseline as well as outperforming slide adaptation.The research leading to these results has received fund-ing from the European Union Seventh Framework Programme (FP7/2007-2013)under grant agreement no 287755 (transLectures) and ICT Policy Support Pro-gramme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme (CIP) under grant agreement no 621030 (EMMA), the Spanish MINECO Active2Trans (TIN2012-31723) research project and the Spanish Government with the FPU scholarships FPU13/06241 and AP2010-4349.Martínez-Villaronga, A.; Del Agua Teba, MA.; Silvestre Cerdà, JA.; Andrés Ferrer, J.; Juan, A. (2014). Language model adaptation for lecture transcription by document retrieval. En Advances in Speech and Language Technologies for Iberian Languages. Springer Verlag (Germany). 129-137. https://doi.org/10.1007/978-3-319-13623-3_14S129137coursera.org: Take the World’s Best Courses, Online, For Free, http://www.coursera.org/poliMedia: Videolectures from the “Universitat Politècnica de València, http://polimedia.upv.es/catalogo/SuperLectures: We take full care of your event video recordings, http://www.superlectures.comtransLectures, https://translectures.eu/transLectures-UPV Toolkit (TLK) for Automatic Speech Recognition, http://translectures.eu/tlkUdacity: Learn, Think, Do, http://www.udacity.com/Videolectures.NET: Exchange Ideas and Share Knowledge, http://www.videolectures.net/del-Agua, M.A., Giménez, A., Serrano, N., Andrés-Ferrer, J., Civera, J., Sanchis, A., Juan, A.: The translectures-UPV toolkit. In: Navarro Mesa, J.L., Giménez, A.O., Teixeira, A. (eds.) IberSPEECH 2014. LNCS (LNAI), vol. 8854, pp. 269–278. Springer, Heidelberg (2014)Chang, P.C., Shan Lee, L.: Improved language model adaptation using existing and derived external resources. In: Proc. of ASRU, pp. 531–536 (2003)Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. Computer Speech & Language 13(4), 359–393 (1999)Jelinek, F., Mercer, R.L.: Interpolated Estimation of Markov Source Parameters from Sparse Data. In: Proc. of the Workshop on Pattern Recognition in Practice, pp. 381–397 (1980)Ketterl, M., Schulte, O.A., Hochman, A.: Opencast matterhorn: A community-driven open source solution for creation, management and distribution of audio and video in academia. In: Proc. of ISM, pp. 687–692 (2009)Kneser, R., Ney, H.: Improved Backing-off for M-gram Language Modeling. In: Proc. of ICASSP, pp. 181–184 (1995)Lecorv, G., Gravier, G., Sbillot, P.: An unsupervised web-based topic language model adaptation method. In: Proc. of ICASSP 2008, pp. 5081–5084 (2008)Martínez-Villaronga, A., del Agua, M.A., Andrés-Ferrer, J., Juan, A.: Language model adaptation for video lectures transcription. In: Proc. of ICASSP, pp. 8450–8454 (2013)Munteanu, C., Penn, G., Baecker, R.: Web-based language modelling for automatic lecture transcription. In: Proc. of INTERSPEECH, pp. 2353–2356 (2007)Rogina, I., Schaaf, T.: Lecture and presentation tracking in an intelligent meeting room. In: Proc of ICMI, pp. 47–52 (2002)Schlippe, T., Gren, L., Vu, N.T., Schultz, T.: Unsupervised language model adaptation for automatic speech recognition of broadcast news using web 2.0, pp. 2698–2702 (2013)Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in context-dependent deep neural networks for conversational speech transcription. In: Proc. of ASRU, pp. 24–29 (2011)Silvestre, J.A., et al.: Translectures. In: Proc. of IberSPEECH 2012, pp. 345–351 (2012)Smith, R.: An overview of the tesseract ocr engine. In: Proc. of ICDAR 2007, pp. 629–633 (2007)Stolcke, A.: SRILM – an extensible language modeling toolkit. In: Proc. of ICSLP, pp. 901–904 (2002)Tsiartas, A., Georgiou, P., Narayanan, S.: Language model adaptation using www documents obtained by utterance-based queries. In: Proc. of ICASSP, pp. 5406–5409 (2010

The TransLectures-UPV Toolkit

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-13623-3_28Over the past few years, online multimedia educational repositories have increased in number and popularity. The main aim of the transLectures project is to develop cost-effective solutions for producing accurate transcriptions and translations for large video lecture repositories, such as VideoLectures.NET or the Universitat Politècnica de València s repository, poliMedia. In this paper, we present the transLectures-UPV toolkit (TLK), which has been specifically designed to meet the requirements of the transLectures project, but can also be used as a conventional ASR toolkit. The main features of the current release include HMM training and decoding with speaker adaptation techniques (fCMLLR). TLK has been tested on the VideoLectures.NET and poliMedia repositories, yielding very competitive results. TLK has been released under the permissive open source Apache License v2.0 and can be directly downloaded from the transLectures website.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755 (transLectures) and ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and InnovationFramework Programme (CIP) under grant agreement no 621030 (EMMA), andthe Spanish MINECO Active2Trans (TIN2012-31723) research project.Del Agua Teba, MA.; Giménez Pastor, A.; Serrano Martinez Santos, N.; Andrés Ferrer, J.; Civera Saiz, J.; Sanchis Navarro, JA.; Juan Císcar, A. (2014). The TransLectures-UPV Toolkit. En Advances in Speech and Language Technologies for Iberian Languages: Second International Conference, IberSPEECH 2014, Las Palmas de Gran Canaria, Spain, November 19-21, 2014. Proceedings. Springer International Publishing. 269-278. https://doi.org/10.1007/978-3-319-13623-3_28S269278Final report on massive adaptation (M36). To be delivered on October 2014 (2014)First report on massive adaptation (M12), https://www.translectures.eu/wp-content/uploads/2013/05/transLectures-D3.1.1-18Nov2012.pdfOpencast Matterhorn, http://opencast.org/matterhorn/sclite - Score speech recognition system output, http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sclite.htmSecond report on massive adaptation (M24), https://www.translectures.eu//wp-content/uploads/2014/01/transLectures-D3.1.2-15Nov2013.pdfTLK: The transLectures-UPV Toolkit, https://www.translectures.eu/tlk/Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains. The Annals of Mathematical Statistics 41(1), 164–171 (1970)Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition. IEEE Transactions on Audio, Speech, and Language Processing 20(1), 30–42 (2012)Digalakis, V., Rtischev, D., Neumeyer, L., Sa, E.: Speaker Adaptation Using Constrained Estimation of Gaussian Mixtures. IEEE Transactions on Speech and Audio Processing 3, 357–366 (1995)Huang, J.T., Li, J., Yu, D., Deng, L., Gong, Y.: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In: Proc. of ICASSP (2013)Munteanu, C., Baecker, R., Penn, G., Toms, E., James, D.: The Effect of Speech Recognition Accuracy Rates on the Usefulness and Usability of Webcast Archives. In: Proc. of CHI, pp. 493–502 (2006)Ney, H., Ortmanns, S.: Progress in dynamic programming search for LVCSR. Proceedings of the IEEE 88(8), 1224–1240 (2000)Ortmanns, S., Ney, H., Eiden, A.: Language-model look-ahead for large vocabulary speech recognition. In: Proc. of ICSLP, vol. 4, pp. 2095–2098 (1996)Ortmanns, S., Ney, H., Aubert, X.: A word graph algorithm for large vocabulary continuous speech recognition. Computer Speech and Language 11(1), 43–72 (1997)Povey, D., et al.: The Kaldi Speech Recognition Toolkit. In: Proc. of ASRU (2011)Rumelhart, D., Hintont, G., Williams, R.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)Rybach, D., et al.: The RWTH Aachen University Open Source Speech Recognition System. In: Proc. Interspeech, pp. 2111–2114 (2009)Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription. In: Proc. of ASRU, pp. 24–29 (2011)Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13(2), 260–269 (1967)Young, S., et al.: The HTK Book. Cambridge University Engineering Department (1995)Young, S.J., Odell, J.J., Woodland, P.C.: Tree-based state tying for high accuracy acoustic modelling. In: Proc. of HLT, pp. 307–312 (1994

TransLectures

transLectures (Transcription and Translation of Video Lectures) is an EU STREP project in which advanced automatic speech recognition and machine translation techniques are being tested on large video lecture repositories. The project began in November 2011 and will run for three years. This paper will outline the project¿s main motivation and objectives, and give a brief description of the two main repositories being considered: VideoLectures.NET and poliMedia. The first results obtained by the UPV group for the poliMedia repository will also be provided.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 287755. Funding was also provided by the Spanish Government (iTrans2 project, TIN2009-14511; FPI scholarship BES-2010-033005; FPU scholarship AP2010-4349)Silvestre Cerdà, JA.; Del Agua Teba, MA.; Garcés Díaz-Munío, GV.; Gascó Mora, G.; Giménez Pastor, A.; Martínez-Villaronga, AA.; Pérez González De Martos, AM.... (2012). TransLectures. IberSPEECH 2012. 345-351. http://hdl.handle.net/10251/3729034535

Multilingualidad en reconocimiento de texto manuscrito

Del Agua Teba, MA. (2010). Multilingualidad en reconocimiento de texto manuscrito. http://hdl.handle.net/10251/10509