A Spanish dataset for reproducible benchmarked offline handwriting recognition

[EN] In this paper, a public dataset for Offline Handwriting Recognition, along with an appropriate evaluation method to provide benchmark indicators at sentence level, is presented. This dataset, called SPA-Sentences, consists of offline handwritten Spanish sentences extracted from 1617 forms produced by the same number of writers. A total of 13,691 sentences comprising around 100,000 word instances out of a vocabulary of 3288 words occur in the collection. Careful attention has been paid to make the baseline experiments both reproducible and competitive. To this end, experiments are based on state-of-the-art recognition techniques combining convolutional blocks with one-dimensional Bidirectional Long Short Term Memory (LSTM) networks using Connectionist Temporal Classification (CTC) decoding. The scripts with the entire experimental setting have been made available. The SPA-Sentences dataset and its baseline evaluation are freely available for research purposes via the institutional University repository. We expect the research community to include this corpus, as is usually done with English IAM and French RIMES datasets, in their battery of experiments when reporting novel handwriting recognition techniques.España Boquera, S.; Castro-Bleda, MJ. (2022). A Spanish dataset for reproducible benchmarked offline handwriting recognition. Language Resources and Evaluation. 56(3):1009-1022. https://doi.org/10.1007/s10579-022-09587-31009102256

The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

[EN] This paper presents the `NoisyOffice¿ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.This research was undertaken as part of the project TIN2017-85854-C4-2-R, jointly funded by the Spanish MINECO and FEDER founds.Castro-Bleda, MJ.; España Boquera, S.; Pastor Pellicer, J.; Zamora Martínez, FJ. (2020). The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing. The Computer Journal. 63(11):1658-1667. https://doi.org/10.1093/comjnl/bxz098S165816676311Bozinovic, R. M., & Srihari, S. N. (1989). Off-line cursive script word recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1), 68-83. doi:10.1109/34.23114Plamondon, R., & Srihari, S. N. (2000). Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84. doi:10.1109/34.824821Vinciarelli, A. (2002). A survey on off-line Cursive Word Recognition. Pattern Recognition, 35(7), 1433-1446. doi:10.1016/s0031-3203(01)00129-7Impedovo, S. (2014). More than twenty years of advancements on Frontiers in handwriting recognition. Pattern Recognition, 47(3), 916-928. doi:10.1016/j.patcog.2013.05.027Baird, H. S. (2007). The State of the Art of Document Image Degradation Modelling. Advances in Pattern Recognition, 261-279. doi:10.1007/978-1-84628-726-8_12Egmont-Petersen, M., de Ridder, D., & Handels, H. (2002). Image processing with neural networks—a review. Pattern Recognition, 35(10), 2279-2301. doi:10.1016/s0031-3203(01)00178-9Marinai, S., Gori, M., & Soda, G. (2005). Artificial neural networks for document analysis and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(1), 23-35. doi:10.1109/tpami.2005.4Rehman, A., & Saba, T. (2012). Neural networks for document image preprocessing: state of the art. Artificial Intelligence Review, 42(2), 253-273. doi:10.1007/s10462-012-9337-zLazzara, G., & Géraud, T. (2013). Efficient multiscale Sauvola’s binarization. International Journal on Document Analysis and Recognition (IJDAR), 17(2), 105-123. doi:10.1007/s10032-013-0209-0Fischer, A., Indermühle, E., Bunke, H., Viehhauser, G., & Stolz, M. (2010). Ground truth creation for handwriting recognition in historical documents. Proceedings of the 8th IAPR International Workshop on Document Analysis Systems - DAS ’10. doi:10.1145/1815330.1815331Belhedi, A., & Marcotegui, B. (2016). Adaptive scene‐text binarisation on images captured by smartphones. IET Image Processing, 10(7), 515-523. doi:10.1049/iet-ipr.2015.0695Kieu, V. C., Visani, M., Journet, N., Mullot, R., & Domenger, J. P. (2013). An efficient parametrization of character degradation model for semi-synthetic image generation. Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing - HIP ’13. doi:10.1145/2501115.2501127Fischer, A., Visani, M., Kieu, V. C., & Suen, C. Y. (2013). Generation of learning samples for historical handwriting recognition using image degradation. Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing - HIP ’13. doi:10.1145/2501115.2501123Journet, N., Visani, M., Mansencal, B., Van-Cuong, K., & Billy, A. (2017). DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images. Journal of Imaging, 3(4), 62. doi:10.3390/jimaging3040062Walker, D., Lund, W., & Ringger, E. (2012). A synthetic document image dataset for developing and evaluating historical document processing methods. Document Recognition and Retrieval XIX. doi:10.1117/12.912203Dong, C., Loy, C. C., He, K., & Tang, X. (2016). Image Super-Resolution Using Deep Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295-307. doi:10.1109/tpami.2015.2439281Suzuki, K., Horiba, I., & Sugie, N. (2003). Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 1582-1596. doi:10.1109/tpami.2003.1251151Hidalgo, J. L., España, S., Castro, M. J., & Pérez, J. A. (2005). Enhancement and Cleaning of Handwritten Data by Using Neural Networks. Lecture Notes in Computer Science, 376-383. doi:10.1007/11492429_46Pastor-Pellicer, J., España-Boquera, S., Zamora-Martínez, F., Afzal, M. Z., & Castro-Bleda, M. J. (2015). Insights on the Use of Convolutional Neural Networks for Document Image Binarization. Lecture Notes in Computer Science, 115-126. doi:10.1007/978-3-319-19222-2_10España-Boquera, S., Zamora-Martínez, F., Castro-Bleda, M. J., & Gorbe-Moya, J. (s. f.). Efficient BP Algorithms for General Feedforward Neural Networks. Lecture Notes in Computer Science, 327-336. doi:10.1007/978-3-540-73053-8_33Zamora-Martínez, F., España-Boquera, S., & Castro-Bleda, M. J. (s. f.). Behaviour-Based Clustering of Neural Networks Applied to Document Enhancement. Lecture Notes in Computer Science, 144-151. doi:10.1007/978-3-540-73007-1_18Graves, A., Fernández, S., & Schmidhuber, J. (2007). Multi-dimensional Recurrent Neural Networks. Artificial Neural Networks – ICANN 2007, 549-558. doi:10.1007/978-3-540-74690-4_56Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern Recognition, 33(2), 225-236. doi:10.1016/s0031-3203(99)00055-2Pastor-Pellicer, J., Castro-Bleda, M. J., & Adelantado-Torres, J. L. (2015). esCam: A Mobile Application to Capture and Enhance Text Images. Lecture Notes in Computer Science, 601-604. doi:10.1007/978-3-319-19222-2_5

Aspectos biogeográficos y ecológicos del género Quercus (Fagaceae) en Michoacán, México

Background and Aims:Michoacan is a state with high biodiversity. Taxonomic studies estimate that there are 30 to 37 species of oak in the state; however, there is scarce information about their distribution patterns. In this study, the geographic and ecological distribution patterns of the genus, and its two sections, composed of 33 species (1734 records), were analyzed at municipality level; topographic, climatic, edaphic, and ecological data are included.Methods: Indices of richness, rarity and irreplaceability were calculated to obtain the distribution patterns at the municipal level of genus and both sections. Cluster analyses were performed to identify patterns of geographic, ecologic and elevational distribution of the species. It was tested whether any environmental gradient explains the distributional patterns.Key results: The highest specific richness of the Quercus genus is located in the Trans-Mexican Volcanic Belt (TMVB) and Sierra Madre del Sur (SMS), where humid and subhumid temperate environments are prevalent; the highest rareness occurred in the east-northeast of the TMVB. With respect to elevation, section Quercus shows larger distribution ranges than section Lobatae. Based on elevation, the oaks can be classified into low (600-1900 m a.s.l.) and high mountains (2000-3100 m a.s.l.) with a strong turnover of species between 1900-2000 m a.s.l. Five groups of species are identified, based on their environmental similarity, which coincide with physiographic regions.Conclusions: The municipal richness of oaks is correlated with topographic and aridity heterogeneity at the generic level, mainly in the Lobatae section. Considering that greater environmental heterogeneity corresponds to a greater diversity of species, it is recommended to increase the sampling effort in municipalities that have greater environmental heterogeneity. Intensive fieldwork in the SMS is suggested. There are still unresolved taxonomic problems that should be studied in detail.Antecedentes y Objetivos: Michoacán es un estado de alta biodiversidad. Estudios taxonómicos estiman que existen de 30 a 37 especies de encinos en el estado; sin embargo, es incipiente la información sobre sus patrones de distribución. En este estudio, se analizaron los patrones de distribución geográfica y ecológica del género, y de sus dos secciones, compuesto por 33 especies (1734 registros), a nivel municipal; se incluye información topográfica, climática, edáfica y ecológica.Métodos: Se calcularon índices de riqueza, rareza e irremplazabilidad para obtener los patrones de distribución municipal del género y de ambas secciones. Se realizaron análisis de agrupamiento para identificar patrones de distribución geográfica, ecológica y altitudinal. Se probó si algún gradiente ambiental explica los patrones de distribución.Resultados clave: En la Faja Volcánica Transmexicana (FVTM) y en la Sierra Madre del Sur (SMS) se localiza la mayor riqueza específica del género Quercus, donde predominan climas templados húmedos a subhúmedos; el centro de mayor rareza estatal se encuentra en el este-noreste de la FVTM. En relación con la altitud, las especies de la sección Quercus tienen amplios intervalos de distribución en comparación con las de la sección Lobatae. La composición de encinos difiere en áreas de baja (600-1900 m s.n.m.) y alta montaña (2000-3100 m s.n.m.) con un recambio importante de especies entre 1900-2000 m s.n.m. Se identifican cinco grupos de especies, con base en su similitud ambiental, que coinciden con regiones fisiográficas.Conclusiones: La riqueza municipal de encinos está correlacionada de manera positiva con la heterogeneidad topográfica y de aridez, principalmente en la sección Lobatae. Considerando que a mayor heterogeneidad ambiental corresponde mayor riqueza de especies, se recomienda aumentar el esfuerzo de muestreo en municipios que tienen una amplia heterogeneidad ambiental. Se sugiere, además, un intenso trabajo de campo en la SMS. Aún hay problemas taxonómicos que deberían ser estudiados detalladamente

On the modification of binarization algorithms to retain grayscale information for handwritten text recognition

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_24S208215Drida, 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.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1998)Khurshid, K., Siddiqi, I., Faure, C., Vincent, N.: Comparison of niblack inspired binarization methods for ancient documents. In: Berkner, K., Likforman-Sulem, L. (eds.) 16th Document Recognition and Retrieval Conference, DRR 2009, SPIE Proceedings, vol. 7247, pp. 1–10. SPIE, San Jose (18–22 January 2009). doi: 10.1117/12.805827Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling, Detroit, USA, vol. 1, pp. 181–184 (1995)Marti, U., Bunke, H.: Using a statistical language model to improve the preformance of an HMM-based cursive handwriting recognition system. IJPRAI 15(1), 65–90 (2001)Niblack, W.: An Introduction to Digital Image Processing, pp. 115–116. Prentice-Hall, Englewood Cliffs (1986)Romero, V., Fornés, A., Serrano, N., Sánchez, J., Toselli, A., Frinken, V., Vidal, E., Lladós, J.: The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. Pattern Recogn. 46, 1658–1669 (2013). doi: 10.1016/j.patcog.2012.11.024España-Boquera, S., Castro-Bleda, M.J., Gorbe-Moya, J., Zamora-Martínez, F.: Improving offline handwriting text recognition with hybrid hmm/ann models. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 767–779 (2011)Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recog. 33(2), 225–236 (2000). doi: 10.1016/S0031-3203(99)00055-2Shafait, F., Keysers, D., Breuel, T.M.: Efficient implementation of local adaptive thresholding techniques using integral images. In: Proceedings of the SPIE 6815, Document Recognition and Retrieval XV, 681510, pp. 1–6, January 2008. doi: 10.1117/12.767755Toselli, A.H., 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 Recog. Artif. Intell. 18(4), 519–539 (2004). doi: 10.1142/S021800140400334

Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training

[EN] This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different length in order to ease the use of a fallback mechanism together with the precomputation of softmax normalization constants for these inputs. The proposed approach is empirically validated, showing their capability to emulate lower order N-grams with a single Neural Network. A machine translation task shows that the proposed model constitutes a good solution to the normalization cost of the output softmax layer of Neural Networks, for some practical cases, without a significant impact in performance while improving the system speed.This work was partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R (to MJCB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Zamora Martínez, FJ.; España Boquera, S.; Castro-Bleda, MJ.; Palacios Corella (2018). Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training. PLoS ONE. 13(7). https://doi.org/10.1371/journal.pone.0200884S13

Transcrição humana ou assistência interativa computadorizada: reconhecimento automático, anotação e edição erudite no século XXI

Computer assisted transcription tools can speed up the initial process of reading and transcribing texts. At the same time, new annotation tools open new ways of accessing the text in its graphical form. The balance and value of each method still needs to be explored. STATE, a complete assisted transcription system for ancient documents, was presented to the audience of the 2013 International Medieval Congress at Leeds. The system offers a multimodal interaction environment to assist humans in transcribing ancient documents: the user can type, write on the screen with a stylus, or utter a word. When one of these actions is used to correct an erroneous word, the system uses this new information to look for other mistakes in the rest of the line. The system is modular, composed of different parts: one part creates projects from a set of images of documents, another part controls an automatic transcription system, and the third part allows the user to interact with the transcriptions and easily correct them as needed. This division of labour allows great flexibility for organising the work in a team of transcribers.Las herramientas de ayuda a la transcripción automática pueden acelerar el proceso inicial de la lectura y transcripción de textos. Al mismo tiempo, las nuevas herramientas de anotación aportan nuevas formas de acceder al texto en su forma original gráfica. Sin embargo, todavía es necesario evaluar las bondades y capacidades de los distintos métodos. STATE, un completo sistema de asistencia a la transcripción de documentos antiguos, se presentó a la audiencia del International Medieval Congress de 2013 celebrado en Leeds. El sistema ofrece un entorno de interacción multimodal para ayudar a las personas en la transcripción de documentos antiguos: el usuario puede teclear, escribir en la pantalla con un lápiz óptico o corregir usando la voz. Cada vez que el usuario cambia de esta forma una palabra, el sistema utiliza la corrección para buscar errores en el resto de la línea. El sistema está dividido en diferentes módulos: uno crea proyectos a partir de un conjunto de imágenes de documentos, otro módulo controla el sistema de transcripción automática, y un tercer módulo permite al usuario interactuar con las transcripciones y corregirlas fácilmente cuando sea necesario. Esta división de las tareas permite una gran flexibilidad para organizar el trabajo de los transcriptores en equipo.Work supported by the Spanish Government (TIN2010-18958) and the Generalitat Valenciana (Prometeo/2010/028)

Neural network language models to select the best translation

The quality of translations produced by statistical machine translation (SMT) systems crucially depends on the generalization ability provided by the statistical models involved in the process. While most modern SMT systems use n-gram models to predict the next element in a sequence of tokens, our system uses a continuous space language model (LM) based on neural networks (NN). In contrast to works in which the NN LM is only used to estimate the probabilities of shortlist words (Schwenk 2010), we calculate the posterior probabilities of out-of-shortlist words using an additional neuron and unigram probabilities. Experimental results on a small Italian- to-English and a large Arabic-to-English translation task, which take into account different word history lengths (n-gram order), show that the NN LMs are scalable to small and large data and can improve an n-gram-based SMT system. For the most part, this approach aims to improve translation quality for tasks that lack translation data, but we also demonstrate its scalability to large-vocabulary tasks.Khalilov, M.; Fonollosa, JA.; Zamora-Mart Nez, F.; Castro Bleda, MJ.; España Boquera, S. (2013). Neural network language models to select the best translation. Computational Linguistics in the Netherlands Journal. (3):217-233. http://hdl.handle.net/10251/46629S217233

Handwriting recognition by using deep learning to extract meaningful features

[EN] Recent improvements in deep learning techniques show that deep models can extract more meaningful data directly from raw signals than conventional parametrization techniques, making it possible to avoid specific feature extraction in the area of pattern recognition, especially for Computer Vision or Speech tasks. In this work, we directly use raw text line images by feeding them to Convolutional Neural Networks and deep Multilayer Perceptrons for feature extraction in a Handwriting Recognition system. The proposed recognition system, based on Hidden Markov Models that are hybridized with Neural Networks, has been tested with the IAM Database, achieving a considerable improvement.Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R.Pastor Pellicer, J.; Castro-Bleda, MJ.; España Boquera, S.; Zamora-Martinez, FJ. (2019). Handwriting recognition by using deep learning to extract meaningful features. 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M., & Srihari, S. N. (1989). Off-line cursive script word recognition. 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Confidence- and margin-based MMI/MPE discriminative training for off-line handwriting recognition. International Journal on Document Analysis and Recognition (IJDAR), 14(3), 273-288. doi:10.1007/s10032-011-0160-xEspaña-Boquera, S., Castro-Bleda, M. J., Gorbe-Moya, J., & Zamora-Martinez, F. (2011). Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4), 767-779. doi:10.1109/tpami.2010.141A. Graves, S. Fernández, F. Gomez and J. Schmidhuber, Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks, in: 23rd International Conference on Machine Learning (ICML), ACM, 2006, pp. 369–376.A. Graves and N. Jaitly, Towards end-to-end speech recognition with recurrent neural networks, in: 31st International Conference on Machine Learning (ICML), 2014, pp. 1764–1772.Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). 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Modern vs Diplomatic Transcripts for Historical Handwritten Text Recognition

Abstract: The transcription of handwritten documents is useful to make their contents accessible to the general public. However, so far automatic transcription of historical documents has mostly focused on producing diplomatic transcripts, even if such transcripts are often only understandable by experts. Main difficulties come from the heavy use of extremely abridged and tangled abbreviations and archaic or outdated word forms. Here we study different approaches to train optical models which allow to recognize historic document images containing archaic and abbreviated handwritten text and produce modernized transcripts with expanded abbreviations. Experiments comparing the performance of the different approaches proposed are carried out on a document collection related with Spanish naval commerce during the XV–XIX centuries, which includes extremely difficult handwritten text image

An iterative multimodal framework for the transcription of handwritten historical documents

[EN] The transcription of historical documents is one of the most interesting tasks in which Handwritten Text Recognition can be applied, due to its interest in humanities research. One alternative for transcribing the ancient manuscripts is the use of speech dictation by using Automatic Speech Recognition techniques. In the two alternatives similar models (Hidden Markov Models and n-grams) and decoding processes (Viterbi decoding) are employed, which allows a possible combination of the two modalities with little diffi- culties. In this work, we explore the possibility of using recognition results of one modality to restrict the decoding process of the other modality, and apply this process iteratively. Results of these multimodal iterative alternatives are significantly better than the baseline uni-modal systems and better than the non-iterative alternatives. 2012 Elsevier B.V. All rights reserved.Work supported by the EC (FEDER/FSE) and the Spanish MEC/MICINN under the MIPRCV ’’Consolider Ingenio 2010’’ program (CSD2007-00018), iTrans2 (TIN2009–14511) and MITTRAL (TIN2009-14633-C03–01) projects. Also supported by the Spanish MITyC under the erudito.com (TSI-020110-2009-439) project and by the Generalitat Valenciana under grant GV/2010/067, and by the UPV under project PAID-05-11-2779 and grant UPV/2009/2851.Alabau, V.; Martínez Hinarejos, CD.; Romero Gómez, V.; Lagarda Arroyo, AL. (2014). An iterative multimodal framework for the transcription of handwritten historical documents. Pattern Recognition Letters. 35:195-203. https://doi.org/10.1016/j.patrec.2012.11.007S1952033