Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization

An efficient algorithm for recurrent neural network training is presented. The approach increases the training speed for tasks where a length of the input sequence may vary significantly. The proposed approach is based on the optimal batch bucketing by input sequence length and data parallelization on multiple graphical processing units. The baseline training performance without sequence bucketing is compared with the proposed solution for a different number of buckets. An example is given for the online handwriting recognition task using an LSTM recurrent neural network. The evaluation is performed in terms of the wall clock time, number of epochs, and validation loss value.Comment: 4 pages, 5 figures, Comments, 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, 201

Character-level interaction in multimodal computer-assisted transcription of text images

“The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-21257-4_85To date, automatic handwriting text recognition systems are far from being perfect and heavy human intervention is often required to check and correct the results of such systems. As an alternative, an interactive framework that integrates the human knowledge into the transcription process has been presented in previous works. In this work, multimodal interaction at character-level is studied. Until now, multimodal interaction had been studied only at whole-word level. However, character-level pen-stroke interactions may lead to more ergonomic and friendly interfaces. Empirical tests show that this approach can save significant amounts of user effort with respect to both fully manual transcription and non-interactive post-editing correction.Work supported by the Spanish Government (MICINN and “Plan E”) under the MITTRAL (TIN2009-14633-C03-01) research project and under the research programme Consolider Ingenio 2010: MIPRCV (CSD2007-00018), and by the Generalitat Valenciana under grant Prometeo/2009/014.Martín-Albo Simón, D.; Romero Gómez, V.; Toselli ., AH.; Vidal, E. (2011). Character-level interaction in multimodal computer-assisted transcription of text images. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 684-691. https://doi.org/10.1007/978-3-642-21257-4S68469

Training of On-line Handwriting Text Recognizers with Synthetic Text Generated Using the Kinematic Theory of Rapid Human Movements

©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A method for automatic generation of synthetic handwritten words is presented which is based in the Kinematic Theory and its Sigma-lognormal model. To generate a new synthetic sample, first a real word is modelled using the Sigmalognormal model. Then the Sigma-lognormal parameters are randomly perturbed within a range, introducing human-like variations in the sample. Finally, the velocity function is recalculated taking into account the new parameters. The synthetic words are then used as training data for a Hidden Markov Model based on-line handwritten recognizer. The experimental results confirm the great potential of the Kinematic Theory of rapid human movements applied to writer adaptation.This work was partially supported by the Universitat Politècnica de València under the PMIA-2013 scholarship, the Spanish MEC under FPU scholarship AP2010-0575, the EU’s 7th Framework Programme (FP7/2007-2013) under grant agreement n. 600707 (tranScriptorium) and n. 287576 (CasMaCat) and the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant RGPIN-915.Martín-Albo Simón, D.; Plamondon, R.; Vidal Ruiz, E. (2014). Training of On-line Handwriting Text Recognizers with Synthetic Text Generated Using the Kinematic Theory of Rapid Human Movements. IEEE. https://doi.org/10.1109/ICFHR.2014.97

Self-supervised adaptation for on-line script text recognition

We have recently developed in our lab a text recognizer for on-line texts written on a touch-terminal. We present in this paper several strategies to adapt this recognizer in a self-supervised way to a given writer and compare them to the supervised adaptation scheme. The baseline system is based on the activation-verification cognitive model. We have designed this recognizer to be writer-independent but it may be adapted to be writer-dependent in order to increase the recognition speed and rate. The classification expert can be iteratively modified in order to learn the particularities of a writer. The best self-supervised adaptation strategy is called prototype dynamic management and gets good results, close to those of the supervised methods. The combination of supervised and self-supervised strategies increases accuracy again. Results, presented on a large database of 90 texts (5,400 words) written by 38 different writers are very encouraging with an error rate lower than 10%

Generalization Capacity of Handwritten Outlier Symbols Rejection with Neural Network

http://www.suvisoft.comDifferent problems of generalization of outlier rejection exist depending of the context. In this study we firstly define three different problems depending of the outlier availability during the learning phase of the classifier. Then we propose different solutions to reject outliers with two main strategies: add a rejection class to the classifier or delimit its knowledge to better reject what it has not learned. These solutions are compared with ROC curves to recognize handwritten digits and reject handwritten characters. We show that delimiting knowledge of the classifier is important and that using only a partial subset of outliers do not perform a good reject option

Coopération modélisation : discrimination pour la reconnaissance d'écriture manuscrite

-Reconnaître l'écriture manuscrite est un problème d'une telle complexité qu'il est devenu courant de faire coopérer plusieurs algorithmes de classification. Dans cet article, nous présentons un classifieur hybride original. Un premier expert de modélisation détermine les deux classes les plus pertinentes en comparant le symbole inconnu à un ensemble exhaustif de symboles. Le second, discriminant, permet de lever les ambiguïtés. Cette architecture hybride exploite le fait que la "bonne" classe appartient le plus souvent aux deux classes les plus pertinentes trouvées par le premier classifieur. Les expérimentations, conduites sur une base de test de 20000 formes (62 classes), montrent que l'apport relatif de la coopération s'élève à 30%

Improving on-line handwritten recognition in interactive machine translation

[EN] On-line handwriting text recognition (HTR) could be used as a more natural way of interaction in many interactive applications. However, current HTR technology is far from developing error-free systems and, consequently, its use in many applications is limited. Despite this, there are many scenarios, as in the correction of the errors of fully-automatic systems using HTR in a post-editing step, in which the information from the specific task allows to constrain the search and therefore to improve the HTR accuracy. For example, in machine translation (MT), the on-line HTR system can also be used to correct translation errors. The HTR can take advantage of information from the translation problem such as the source sentence that is translated, the portion of the translated sentence that has been supervised by the human, or the translation error to be amended. Empirical experimentation suggests that this is a valuable information to improve the robustness of the on-line HTR system achieving remarkable results.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreement no. 287576 (CasMaCat), from the EC (FEDER/FSE), and from the Spanish MEC/MICINN under the Active2Trans (TIN2012-31723) project. It is also supported by the Generalitat Valenciana under Grant ALMPR (Prometeo/2009/01) and GV/2010/067.Alabau Gonzalvo, V.; Sanchis Navarro, JA.; Casacuberta Nolla, F. (2014). Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition. 47(3):1217-1228. https://doi.org/10.1016/j.patcog.2013.09.035S1217122847