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. In: IEEE Spoken Language Technology Workshop, SLT 2008, pp. 273–276 (December 2008)Brakensiek, A., Rottl, J., Kosmala, A., Rigoll, G.: Off-Line handwriting recognition using various hybrid modeling techniques and character N-Grams. In: 7th International Workshop on Frontiers in Handwritten Recognition, pp. 343–352 (2000)Zamora, F., Castro, M.J., España, S., Gorbe, J.: Unconstrained offline handwriting recognition using connectionist character n-grams. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (July 2010)Marti, U.V., Bunke, H.: The IAM-database: an English sentence database for off-line handwriting recognition. IJDAR, 39–46 (2002)Schultz, T., Kirchhoff, K.: Multilingual Speech Processing (2006)Stolcke, A.: SRILM – an extensible language modeling toolkit. 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

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

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

Interactive handwriting recognition with limited user effort

The final publication is available at Springer via http://dx.doi.org/10.1007/s10032-013-0204-5[EN] Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. Although post-editing automatic recognition of handwritten text is feasible, it is not clearly better than simply ignoring it and transcribing the document from scratch. A more effective approach is to follow an interactive approach in which both the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Nevertheless, in some applications, the user effort available to transcribe documents is limited and fully supervision of the system output is not realistic. To circumvent these problems, we propose a novel interactive approach which efficiently employs user effort to transcribe a document by improving three different aspects. Firstly, the system employs a limited amount of effort to solely supervise recognised words that are likely to be incorrect. Thus, user effort is efficiently focused on the supervision of words for which the system is not confident enough. Secondly, it refines the initial transcription provided to the user by recomputing it constrained to user supervisions. In this way, incorrect words in unsupervised parts can be automatically amended without user supervision. Finally, it improves the underlying system models by retraining the system from partially supervised transcriptions. In order to prove these statements, empirical results are presented on two real databases showing that the proposed approach can notably reduce user effort in the transcription of handwritten text in (old) documents.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 (MICINN, MITyC, "Plan E", under Grants MIPRCV "Consolider Ingenio 2010", MITTRAL (TIN2009-14633-C03-01), erudito.com (TSI-020110-2009-439), iTrans2 (TIN2009-14511), and FPU (AP2007-02867), and the Generalitat Valenciana (Grants Prometeo/2009/014 and GV/2010/067).Serrano Martinez Santos, N.; Giménez Pastor, A.; Civera Saiz, J.; Sanchis Navarro, JA.; Juan Císcar, A. (2014). Interactive handwriting recognition with limited user effort. International Journal on Document Analysis and Recognition. 17(1):47-59. https://doi.org/10.1007/s10032-013-0204-5S4759171Agua, M., Serrano, N., Civera, J., Juan, A.: Character-based handwritten text recognition of multilingual documents. In: Proceedings of Advances in Speech and Language Technologies for Iberian Languages (IBERSPEECH 2012), Madrid (Spain), pp. 187–196 (2012)Ahn, L.V., Maurer, B., Mcmillen, C., Abraham, D., Blum, M.: reCAPTCHA: human-based character recognition via web security measures. Science 321, 1465–1468 (2008)Barrachina, S., Bender, O., Casacuberta, F., Civera, J., Cubel, E., Khadivi, S., Lagarda, A.L., Ney, H., Tomás, J., Vidal, E.: Statistical approaches to computer-assisted translation. Comput. Linguist. 35(1), 3–28 (2009)Bertolami, R., Bunke, H.: Hidden markov model-based ensemble methods for offline handwritten text line recognition. Pattern Recognit. 41, 3452–3460 (2008)Bunke, H., Bengio, S., Vinciarelli, A.: Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 709–720 (2004)Dreuw, P., Jonas, S., Ney, H.: White-space models for offline Arabic handwriting recognition. In: Proceedings of the 19th International Conference on, Pattern Recognition, pp. 1–4 (2008)Efron, B., Tibshirani, R.J.: An introduction to bootstrap. Chapman and Hall/CRC, London (1994)Fischer, A., Wuthrich, M., Liwicki, M., Frinken, V., Bunke, H., Viehhauser, G., Stolz, M.: Automatic transcription of handwritten medieval documents. In: Proceedings of the 15th International Conference on Virtual Systems and Multimedia, pp. 137–142 (2009)Frinken, V., Bunke, H.: Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, Barcelona (Spain), pp. 31–35 (2009)Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)Hakkani-Tür, D., Riccardi, G., Tur, G.: An active approach to spoken language processing. ACM Trans. Speech Lang. Process. 3, 1–31 (2006)Kristjannson, T., Culotta, A., Viola, P., McCallum, A.: Interactive information extraction with constrained conditional random fields. In: Proceedings of the 19th Natural Conference on Artificial Intelligence, San Jose, CA (USA), pp. 412–418 (2004)Laurence Likforman-Sulem, A.Z., Taconet, B.: Text line segmentation of historical documents: a survey. Int. J. Doc. Anal. Recognit. 9, 123–138 (2007)Le Bourgeois, F., Emptoz, H.: Debora: digital access to books of the renaissance. Int. J. Doc. Anal. Recognit. 9, 193–221 (2007)Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)Neal, R.M., Hinton, G.E.: Learning in graphical models. In: A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants, Chap. MIT Press, Cambridge, MA, USA, pp. 355–368 (1999)Pérez, D., Tarazón, L., Serrano, N., Ramos-Terrades, O., Juan, A.: The GERMANA database. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, Barcelona (Spain), pp. 301–305 (2009)Plötz, T., Fink, G.A.: Markov models for offline handwriting recognition: a survey. Int. J. Doc. Anal. Recognit. 12(4), 269–298 (2009)Quiniou, S., Cheriet, M., Anquetil, E.: Error handling approach using characterization and correction steps for handwritten document analysis. Int. J. Doc. Anal. Recognit. 15(2), 125–141 (2012)Rodríguez, L., García-Varea, I., Vidal, E.: Multi-modal computer assisted speech transcription. In: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, ACM, New York, NY, USA, pp. 30:1–30:7 (2010)Serrano, N., Pérez, D., Sanchis, A., Juan, A.: Adaptation from partially supervised handwritten text transcriptions. In: Proceedings of the 11th International Conference on Multimodal Interfaces and the 6th Workshop on Machine Learning for Multimodal Interaction, Cambridge, MA (USA), pp. 289–292 (2009)Serrano, N., Castro, F., Juan, A.: The RODRIGO database. In: Proceedings of the 7th International Conference on Language Resources and Evaluation, Valleta (Malta), pp. 2709–2712 (2010)Serrano, N., Giménez, A., Sanchis, A., Juan, A.: Active learning strategies for handwritten text transcription. In: Proceedings of the 12th International Conference on Multimodal Interfaces and the 7th Workshop on Machine Learning for Multimodal, Interaction, Beijing (China) (2010)Serrano, N., Sanchis, A., Juan, A.: Balancing error and supervision effort in interactive-predictive handwriting recognition. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, Hong Kong (China), pp. 373–376 (2010)Serrano, N., Tarazón, L., Pérez, D., Ramos-Terrades, O., Juan, A.: The GIDOC prototype. In: Proceedings of the 10th International Workshop on Pattern Recognition in Information Systems, Funchal (Portugal), pp. 82–89 (2010)Settles, B.: Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison (2009)Tarazón, L., Pérez, D., Serrano, N., Alabau, V., Ramos-Terrades, O., Sanchis, A., Juan, A.: Confidence measures for error correction in interactive transcription of handwritten text. In: Proceedings of the 15th International Conference on Image Analysis, Processing, Vietri sul Mare (Italy) (2009)Toselli, A., Juan, A., Keysers, D., González, J., Salvador, I., Ney, H., Vidal, E., Casacuberta, F.: Integrated handwriting recognition and interpretation using finite-state models. Int. J. Pattern Recognit. Artif. Intell. 18(4), 519–539 (2004)Toselli, A., Romero, V., Rodríguez, L., Vidal, E.: Computer assisted transcription of handwritten text. In: Proceedings of the 9th International Conference on Document Analysis and Recognition, Curitiba (Brazil), pp. 944–948 (2007)Valor, J., Pérez, A., Civera, J., Juan, A.: Integrating a state-of-the-art ASR system into the opencast Matterhorn platform. In: Proceedings of the Advances in Speech and Language Technologies for Iberian Languages (IBERSPEECH 2012), Madrid (Spain), pp. 237–246 (2012)Wessel, F., Ney, H.: Unsupervised training of acoustic models for large vocabulary continuous speech recognition. IEEE Trans Speech Audio Process 13(1), 23–31 (2005