Arabic Handwritten Word Recognition based on Bernoulli Mixture HMMs

This thesis presents new approaches in off-line Arabic Handwriting Recognition based on conventional Bernoulli Hidden Markov models. Until now, the off-line handwriting recognition, in particular, the Arabic handwriting recognition is still far away form being perfect. Hidden Markov Models (HMMs) are now widely used for off-line handwriting recognition in many languages and, in particular, in Arabic. As in speech recognition, they are usually built from shared, embedded HMMs at symbol level, in which state-conditional probability density functions are modeled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kind of features should be used and, indeed, very different features sets are in use today. Among them, we have recently proposed to simply use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli (mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled with Bernoulli mixtures. The idea is to by-pass feature extraction and ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. In this thesis, we review this idea along with some extensions that are currently providing state-of-the-art results on Arabic handwritten word recognition.Alkhoury, I. (2010). Arabic Handwritten Word Recognition based on Bernoulli Mixture HMMs. http://hdl.handle.net/10251/11478Archivo delegad

Arabic Text Recognition and Machine Translation

[EN] Research on Arabic Handwritten Text Recognition (HTR) and Arabic-English Machine Translation (MT) has been usually approached as two independent areas of study. However, the idea of creating one system that combines both areas together, in order to generate English translation out of images containing Arabic text, is still a very challenging task. This process can be interpreted as the translation of Arabic images. In this thesis, we propose a system that recognizes Arabic handwritten text images, and translates the recognized text into English. This system is built from the combination of an HTR system and an MT system. Regarding the HTR system, our work focuses on the use of Bernoulli Hidden Markov Models (BHMMs). BHMMs had proven to work very well with Latin script. Indeed, empirical results based on it were reported on well-known corpora, such as IAM and RIMES. In this thesis, these results are extended to Arabic script, in particular, to the well-known IfN/ENIT and NIST OpenHaRT databases for Arabic handwritten text. The need for transcribing Arabic text is not only limited to handwritten text, but also to printed text. Arabic printed text might be considered as a simple form of handwritten text version. Thus, for this kind of text, we also propose Bernoulli HMMs. In addition, we propose to compare BHMMs with state-of-the-art technology based on neural networks. A key idea that has proven to be very effective in this application of Bernoulli HMMs is the use of a sliding window of adequate width for feature extraction. This idea has allowed us to obtain very competitive results in the recognition of both Arabic handwriting and printed text. Indeed, a system based on it ranked first at the ICDAR 2011 Arabic recognition competition on the Arabic Printed Text Image (APTI) database. Moreover, this idea has been refined by using repositioning techniques for extracted windows, leading to further improvements in Arabic text recognition. In the case of handwritten text, this refinement improved our system which ranked first at the ICFHR 2010 Arabic handwriting recognition competition on IfN/ENIT. In the case of printed text, this refinement led to an improved system which ranked second at the ICDAR 2013 Competition on Multi-font and Multi-size Digitally Represented Arabic Text on APTI. Furthermore, this refinement was used with neural networks-based technology, which led to state-of-the-art results. For machine translation, the system was based on the combination of three state-of-the-art statistical models: the standard phrase-based models, the hierarchical phrase-based models, and the N-gram phrase-based models. This combination was done using the Recognizer Output Voting Error Reduction (ROVER) method. Finally, we propose three methods of combining HTR and MT to develop an Arabic image translation system. The system was evaluated on the NIST OpenHaRT database, where competitive results were obtained.[ES] El reconocimiento de texto manuscrito (HTR) en árabe y la traducción automática (MT) del árabe al inglés se han tratado habitualmente como dos áreas de estudio independientes. De hecho, la idea de crear un sistema que combine las dos áreas, que directamente genere texto en inglés a partir de imágenes que contienen texto en árabe, sigue siendo una tarea difícil. Este proceso se puede interpretar como la traducción de imágenes de texto en árabe. En esta tesis, se propone un sistema que reconoce las imágenes de texto manuscrito en árabe, y que traduce el texto reconocido al inglés. Este sistema está construido a partir de la combinación de un sistema HTR y un sistema MT. En cuanto al sistema HTR, nuestro trabajo se enfoca en el uso de los Bernoulli Hidden Markov Models (BHMMs). Los modelos BHMMs ya han sido probados anteriormente en tareas con alfabeto latino obteniendo buenos resultados. De hecho, existen resultados empíricos publicados usando corpus conocidos, tales como IAM o RIMES. En esta tesis, estos resultados se han extendido al texto manuscrito en árabe, en particular, a las bases de datos IfN/ENIT y NIST OpenHaRT. En aplicaciones reales, la transcripción del texto en árabe no se limita únicamente al texto manuscrito, sino también al texto impreso. El texto impreso se puede interpretar como una forma simplificada de texto manuscrito. Por lo tanto, para este tipo de texto, también proponemos el uso de modelos BHMMs. Además, estos modelos se han comparado con tecnología del estado del arte basada en redes neuronales. Una idea clave que ha demostrado ser muy eficaz en la aplicación de modelos BHMMs es el uso de una ventana deslizante (sliding window) de anchura adecuada durante la extracción de características. Esta idea ha permitido obtener resultados muy competitivos tanto en el reconocimiento de texto manuscrito en árabe como en el de texto impreso. De hecho, un sistema basado en este tipo de extracción de características quedó en la primera posición en el concurso ICDAR 2011 Arabic recognition competition usando la base de datos Arabic Printed Text Image (APTI). Además, esta idea se ha perfeccionado mediante el uso de técnicas de reposicionamiento aplicadas a las ventanas extraídas, dando lugar a nuevas mejoras en el reconocimiento de texto árabe. En el caso de texto manuscrito, este refinamiento ha conseguido mejorar el sistema que ocupó el primer lugar en el concurso ICFHR 2010 Arabic handwriting recognition competition usando IfN/ENIT. En el caso del texto impreso, este refinamiento condujo a un sistema mejor que ocupó el segundo lugar en el concurso ICDAR 2013 Competition on Multi-font and Multi-size Digitally Represented Arabic Text en el que se usaba APTI. Por otro lado, esta técnica se ha evaluado también en tecnología basada en redes neuronales, lo que ha llevado a resultados del estado del arte. Respecto a la traducción automática, el sistema se ha basado en la combinación de tres tipos de modelos estadísticos del estado del arte: los modelos standard phrase-based, los modelos hierarchical phrase-based y los modelos N-gram phrase-based. Esta combinación se hizo utilizando el método Recognizer Output Voting Error Reduction (ROVER). Por último, se han propuesto tres métodos para combinar los sistemas HTR y MT con el fin de desarrollar un sistema de traducción de imágenes de texto árabe a inglés. El sistema se ha evaluado sobre la base de datos NIST OpenHaRT, donde se han obtenido resultados competitivos.[CA] El reconeixement de text manuscrit (HTR) en àrab i la traducció automàtica (MT) de l'àrab a l'anglès s'han tractat habitualment com dues àrees d'estudi independents. De fet, la idea de crear un sistema que combine les dues àrees, que directament genere text en anglès a partir d'imatges que contenen text en àrab, continua sent una tasca difícil. Aquest procés es pot interpretar com la traducció d'imatges de text en àrab. En aquesta tesi, es proposa un sistema que reconeix les imatges de text manuscrit en àrab, i que tradueix el text reconegut a l'anglès. Aquest sistema està construït a partir de la combinació d'un sistema HTR i d'un sistema MT. Pel que fa al sistema HTR, el nostre treball s'enfoca en l'ús dels Bernoulli Hidden Markov Models (BHMMs). Els models BHMMs ja han estat provats anteriorment en tasques amb alfabet llatí obtenint bons resultats. De fet, existeixen resultats empírics publicats emprant corpus coneguts, tals com IAM o RIMES. En aquesta tesi, aquests resultats s'han estès a la escriptura manuscrita en àrab, en particular, a les bases de dades IfN/ENIT i NIST OpenHaRT. En aplicacions reals, la transcripció de text en àrab no es limita únicament al text manuscrit, sinó també al text imprès. El text imprès es pot interpretar com una forma simplificada de text manuscrit. Per tant, per a aquest tipus de text, també proposem l'ús de models BHMMs. A més a més, aquests models s'han comparat amb tecnologia de l'estat de l'art basada en xarxes neuronals. Una idea clau que ha demostrat ser molt eficaç en l'aplicació de models BHMMs és l'ús d'una finestra lliscant (sliding window) d'amplària adequada durant l'extracció de característiques. Aquesta idea ha permès obtenir resultats molt competitius tant en el reconeixement de text àrab manuscrit com en el de text imprès. De fet, un sistema basat en aquest tipus d'extracció de característiques va quedar en primera posició en el concurs ICDAR 2011 Arabic recognition competition emprant la base de dades Arabic Printed Text Image (APTI). A més a més, aquesta idea s'ha perfeccionat mitjançant l'ús de tècniques de reposicionament aplicades a les finestres extretes, donant lloc a noves millores en el reconeixement de text en àrab. En el cas de text manuscrit, aquest refinament ha aconseguit millorar el sistema que va ocupar el primer lloc en el concurs ICFHR 2010 Arabic handwriting recognition competition usant IfN/ENIT. En el cas del text imprès, aquest refinament va conduir a un sistema millor que va ocupar el segon lloc en el concurs ICDAR 2013 Competition on Multi-font and Multi-size Digitally Represented Arabic Text en el qual s'usava APTI. D'altra banda, aquesta tècnica s'ha avaluat també en tecnologia basada en xarxes neuronals, el que ha portat a resultats de l'estat de l'art. Respecte a la traducció automàtica, el sistema s'ha basat en la combinació de tres tipus de models estadístics de l'estat de l'art: els models standard phrase-based, els models hierarchical phrase-based i els models N-gram phrase-based. Aquesta combinació es va fer utilitzant el mètode Recognizer Output Voting Errada Reduction (ROVER). Finalment, s'han proposat tres mètodes per combinar els sistemes HTR i MT amb la finalitat de desenvolupar un sistema de traducció d'imatges de text àrab a anglès. El sistema s'ha avaluat sobre la base de dades NIST OpenHaRT, on s'han obtingut resultats competitius.Alkhoury, I. (2015). Arabic Text Recognition and Machine Translation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/53029TESI

A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors

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

Bernoulli HMMs for Handwritten Text Recognition

In last years Hidden Markov Models (HMMs) have received significant attention in the task off-line handwritten text recognition (HTR). As in automatic speech recognition (ASR), HMMs are used to model the probability of an observation sequence, given its corresponding text transcription. However, in contrast to what happens in ASR, in HTR there is no standard set of local features being used by most of the proposed systems. In this thesis we propose the use of raw binary pixels as features, in conjunction with models that deal more directly with the binary data. In particular, we propose the use of Bernoulli HMMs (BHMMs), that is, conventional HMMs in which Gaussian (mixture) distributions have been replaced by Bernoulli (mixture) probability functions. The objective is twofold: on the one hand, this allows us to better modeling the binary nature of text images (foreground/background) using BHMMs. On the other hand, this guarantees that no discriminative information is filtered out during feature extraction (most HTR available datasets can be easily binarized without a relevant loss of information). In this thesis, all the HMM theory required to develop a HMM based HTR toolkit is reviewed and adapted to the case of BHMMs. Specifically, we begin by defining a simple classifier based on BHMMs with Bernoulli probability functions at the states, and we end with an embedded Bernoulli mixture HMM recognizer for continuous HTR. Regarding the binary features, we propose a simple binary feature extraction process without significant loss of information. All input images are scaled and binarized, in order to easily reinterpret them as sequences of binary feature vectors. Two extensions are proposed to this basic feature extraction method: the use of a sliding window in order to better capture the context, and a repositioning method in order to better deal with vertical distortions. Competitive results were obtained when BHMMs and proposed methods were applied to well-known HTR databases. In particular, we ranked first at the Arabic Handwriting Recognition Competition organized during the 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2010), and at the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text organized during the 11th International Conference on Document Analysis and Recognition (ICDAR 2011). In the last part of this thesis we propose a method for training BHMM classifiers using In last years Hidden Markov Models (HMMs) have received significant attention in the task off-line handwritten text recognition (HTR). As in automatic speech recognition (ASR), HMMs are used to model the probability of an observation sequence, given its corresponding text transcription. However, in contrast to what happens in ASR, in HTR there is no standard set of local features being used by most of the proposed systems. In this thesis we propose the use of raw binary pixels as features, in conjunction with models that deal more directly with the binary data. In particular, we propose the use of Bernoulli HMMs (BHMMs), that is, conventional HMMs in which Gaussian (mixture) distributions have been replaced by Bernoulli (mixture) probability functions. The objective is twofold: on the one hand, this allows us to better modeling the binary nature of text images (foreground/background) using BHMMs. On the other hand, this guarantees that no discriminative information is filtered out during feature extraction (most HTR available datasets can be easily binarized without a relevant loss of information). In this thesis, all the HMM theory required to develop a HMM based HTR toolkit is reviewed and adapted to the case of BHMMs. Specifically, we begin by defining a simple classifier based on BHMMs with Bernoulli probability functions at the states, and we end with an embedded Bernoulli mixture HMM recognizer for continuous HTR. Regarding the binary features, we propose a simple binary feature extraction process without significant loss of information. All input images are scaled and binarized, in order to easily reinterpret them as sequences of binary feature vectors. Two extensions are proposed to this basic feature extraction method: the use of a sliding window in order to better capture the context, and a repositioning method in order to better deal with vertical distortions. Competitive results were obtained when BHMMs and proposed methods were applied to well-known HTR databases. In particular, we ranked first at the Arabic Handwriting Recognition Competition organized during the 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2010), and at the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text organized during the 11th International Conference on Document Analysis and Recognition (ICDAR 2011). In the last part of this thesis we propose a method for training BHMM classifiers using In last years Hidden Markov Models (HMMs) have received significant attention in the task off-line handwritten text recognition (HTR). As in automatic speech recognition (ASR), HMMs are used to model the probability of an observation sequence, given its corresponding text transcription. However, in contrast to what happens in ASR, in HTR there is no standard set of local features being used by most of the proposed systems. In this thesis we propose the use of raw binary pixels as features, in conjunction with models that deal more directly with the binary data. In particular, we propose the use of Bernoulli HMMs (BHMMs), that is, conventional HMMs in which Gaussian (mixture) distributions have been replaced by Bernoulli (mixture) probability functions. The objective is twofold: on the one hand, this allows us to better modeling the binary nature of text images (foreground/background) using BHMMs. On the other hand, this guarantees that no discriminative information is filtered out during feature extraction (most HTR available datasets can be easily binarized without a relevant loss of information). In this thesis, all the HMM theory required to develop a HMM based HTR toolkit is reviewed and adapted to the case of BHMMs. Specifically, we begin by defining a simple classifier based on BHMMs with Bernoulli probability functions at the states, and we end with an embedded Bernoulli mixture HMM recognizer for continuous HTR. Regarding the binary features, we propose a simple binary feature extraction process without significant loss of information. All input images are scaled and binarized, in order to easily reinterpret them as sequences of binary feature vectors. Two extensions are proposed to this basic feature extraction method: the use of a sliding window in order to better capture the context, and a repositioning method in order to better deal with vertical distortions. Competitive results were obtained when BHMMs and proposed methods were applied to well-known HTR databases. In particular, we ranked first at the Arabic Handwriting Recognition Competition organized during the 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2010), and at the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text organized during the 11th International Conference on Document Analysis and Recognition (ICDAR 2011). In the last part of this thesis we propose a method for training BHMM classifiers using discriminative training criteria, instead of the conventionalMaximum Likelihood Estimation (MLE). Specifically, we propose a log-linear classifier for binary data based on the BHMM classifier. Parameter estimation of this model can be carried out using discriminative training criteria for log-linear models. In particular, we show the formulae for several MMI based criteria. Finally, we prove the equivalence between both classifiers, hence, discriminative training of a BHMM classifier can be carried out by obtaining its equivalent log-linear classifier. Reported results show that discriminative BHMMs clearly outperform conventional generative BHMMs.Giménez Pastor, A. (2014). Bernoulli HMMs for Handwritten Text Recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37978TESI

The UPV Handwriting Recognition and Translation System for OpenHaRT 2013

The NIST Open Handwriting Recognition and Translation Evaluation 2013 (NIST OpenHaRT’13) is a performance evaluation assessing technologies that transcribe and translate text in document images. This evaluation is focused on recognizing Arabic text images and translating them into English. A Handwriting Recognition and Translation system typically consists of a combination of two systems: a Text Recognition system and a Machine Translation system. In this paper, we present the UPV participation in the NIST OpenHaRT 2013 evaluation. For the Text Recognition system we used the TL toolkit for training and recognition. For the Machine Translation system we used the Moses toolkit for training and decoding. Results in this evaluation are challenging and they significantly outperform our previous results in the OpenHaRT 2010 evaluation.Alkhoury, I.; Giménez Pastor, A.; Andrés Ferrer, J.; Juan Císcar, A.; Sánchez Peiró, JA. (2013). The UPV Handwriting Recognition and Translation System for OpenHaRT 2013. US National Institute of Standards and Technology (NIST). http://hdl.handle.net/10251/5439

The use of new technologies to access to handwritten historical information in digital form. Galeón Project

Español: La investigación histórica en archivos obliga a realizar un amplio trabajo de revisión de miles de documentos que, en muchos casos, no tienen relación con el tema de estudio, generando un importante gasto en tiempo y recursos. Para dar respuesta a este problema en relación al estudio del patrimonio arqueológico subacuático, desde el CAS-IAPH se ha ideado el Proyecto Galeón, cuyo objetivo es desarrollar soluciones innovadoras para consultar grandes conjuntos digitalizados de documentos históricos manuscritos. Actualmente no es posible la transcripción automatizada de un gran volumen de imágenes de documentos manuscritos, pero el desarrollo tecnológico en el campo del reconocimiento formal de palabras, puede simplificar este proceso. Para ello se ha ideado un modelo teórico de Búsqueda de Palabras Claves (BPC) basado en Grafos de Palabras (GP), que, además de para el patrimonio cultural marítimo, podría utilizarse para otros temas de investigación. Inglés: Historical research in archives forces to realize an extensive work of reviewing thousands of documents that, in many cases, have no connection with the subject matter, generating a significant expenditure of time and resources. To address this problem in relation to the study of underwater archaeological heritage, from the CAS-IAPH has been devised the Galleon Project, which aims to develop innovative solutions to query large sets of historical documents digitized manuscripts. Nowadays It is not possible the automated transcription of a large volume of images from handwritten documents, but the development in the field of formal recognition of words, can simplify this process. For this we have developed a theoretical model to identify Keywords based on Graphs of Words (GP), which, as well as in the maritime cultural heritage, could be used for any research topic

Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

[EN] The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV) words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs). They show that sub-lexical units outperform word units in terms of Word Error Rate (WER), Character Error Rate (CER) and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs) and Convolutional Recurrent Neural Nets (CRNNs). Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition.Work partially supported by projects READ: Recognition and Enrichment of Archival Documents - 674943 (European Union's H2020) and CoMUN-HaT: Context, Multimodality and User Collaboration in Handwritten Text Processing - TIN2015-70924-C2-1-R (MINECO/FEDER), and a DGA-MRIS (Direction Generale de l'Armement - Mission pour la Recherche et l'Innovation Scientifique) scholarship.Granell, E.; Chammas, E.; Likforman-Sulem, L.; Martínez-Hinarejos, C.; Mokbel, C.; Cirstea, B. (2018). Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks. Journal of imaging. 4(1). https://doi.org/10.3390/jimaging4010015S154