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25th International Conference on pattern recognition, ICPR 2020

作者： Saenz-Gamboa, Jhon Jairo de la Iglesia-Vayá, Maria Gómez, Jon A. Biomedical Imaging Joint Unit Foundation for the Promotion of Health and Biomedical Research FISABIO-CIPF València Spain Pattern Recognition and Human Language Technology research center Universitat Politècnica de València València Spain

ISBN: (纸本)9781728188089

This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, etc. This task is known as semantic segmentation. The approach proposed in this work is based on convolutional neural networks whose output is a mask where each pixel from the input image is classified into one of the possible classes. Classes were defined by radiologists and correspond to structural elements and tissues. The proposed network architectures are variants of the U-Net. Several complementary blocks were used to define the variants: spatial attention models, deep supervision and multi-kernels at input, this last block type is based on the idea of inception. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline. © 2020 IEEE

关键词： Semantic Segmentation

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Sign language Gesture Classification Using Neural Networks 4

Sign Language Gesture Classification Using Neural Networks

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4th International Conference on Advances in Speech and language technologies for Iberian languages, IberSPEECH 2018

作者： Parcheta, Zuzanna Martínez-Hinarejos, Carlos-D. Sciling S.L. Carrer del Riu 321 Pinedo 46012 Spain Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n 46022 Spain

Recent studies have demonstrated the power of neural networks for different fields of artificial intelligence. In most fields, such as machine translation or speech recognition, neural networks outperform previously used methods (Hidden Markov Models with Gaussian Mixtures, Statistical Machine Translation, etc.). In this paper, the efficiency of the LeNet convolutional neural network for isolated word sign language recognition is demonstrated. As a preprocessing step, we apply several techniques to obtain the same dimension for the input that contains gesture information. The performance of these preprocessing techniques on a Spanish Sign language dataset is evaluated. These approaches outperform previously obtained results based on Hidden Markov Models. © 4th International Conference, IberSPEECH 2018.

关键词： Gesture recognition

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Joshua: An Open Source Toolkit for Parsing-based Machine Translation 4

Joshua: An Open Source Toolkit for Parsing-based Machine Tra...

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4th Workshop on Statistical Machine Translation, WMT 2009, immediately preceding the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009

作者： Li, Zhifei Callison-Burch, Chris Dyer, Chris Ganitkevitch, Juri Khudanpur, Sanjeev Schwartz, Lane Thornton, Wren N. G. Weese, Jonathan Zaidan, Omar F. Center For Language And Speech Processing Johns Hopkins University BaltimoreMD United States Computational Linguistics And Information Processing Lab University of Maryland College ParkMD United States Human Language Technology And Pattern Recognition Group RWTH Aachen University Germany Natural Language Processing Lab University of Minnesota MinneapolisMN United States

We describe Joshua, an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for synchronous context free grammars (SCFGs): chart-parsing, ngram language model integration, beamand cube-pruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate training. It uses parallel and distributed computing techniques for scalability. We demonstrate that the toolkit achieves state of the art translation performance on the WMT09 French-English translation task. ©2009 Association for Computational Linguistics.

关键词： Distributed computer systems

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Combining handwriting and speech recognition for transcribing historical handwritten documents

Combining handwriting and speech recognition for transcribin...

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International Conference on Document Analysis and recognition

作者： Emilio Granell Carlos-D. Martínez-Hinarejos Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

Transcription of historical documents is an interesting task for libraries in order to make available their funds. In the lasts years, the use of Handwritten Text recognition allowed paleographs to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is obtaining the draft transcription by dictating the contents to an Automatic Speech recognition system. When both sources (image and speech) are available, a multimodal combination is possible, and an iterative process can be used in order to refine the final hypothesis. In this work, a multimodal combination based on confusion networks is presented. Results on two different sets of data, with different difficulty level, show that the proposed technique provides similar or better draft transcriptions than a previously proposed approach, allowing for a faster transcription process.

关键词： Iterative decoding Acoustics Proposals Laplace equations Integrated optics Optical imaging

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Revisiting Bag-of-Word Metrics to Assess End-To-End Text Image recognition Results

SSRN

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SSRN 2023年

作者： Toselli, Alejandro Héctor Vidal, Enrique Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

The accuracy of traditional, line-based text recognition systems depends only on how well the text in each given line image is recognized. But in recent approaches for end-to-end full-page image transcription, performance is additionally affected by line detection errors and by the order in which lines (or words in general) are provided by the (maybe implicit) layout analysis. Methods based on "bag-of-words" (BoW) have been used in the past to overcome the impact of word-order mismatch, which often leads the traditional edit-distance-based word error rate (WER) to yield pessimistic results. But BoW-based evaluation is not as popular as WER because it is presumed to provide optimistic word error estimates. We revisit the BoW idea and formally introduce evaluation metrics which are tightly and faithfully comparable with WER. We prove properties that qualify these metrics as reliably measures of order-independent word recognition accuracy, and analyse potential causes which may lead them to yield optimistic values. Finally, we report empirical results that favorably compare these metrics with the classical WER for several text recognition approaches and datasets. © 2023, The Authors. All rights reserved.

关键词： Optical character recognition

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ICFHR2014 Competition on Handwritten Text recognition on Transcriptorium Datasets (HTRtS)

ICFHR2014 Competition on Handwritten Text Recognition on Tra...

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International Workshop on Frontiers in Handwriting recognition

作者： Joan Andreu Sánchez Verónica Romero Alejandro H. Toselli Enrique Vidal Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

A contest on Handwritten Text recognition organised in the context of the ICFHR 2014 conference is described. Two tracks with increased freedom on the use of training data were proposed and three research groups participated in these two tracks. The handwritten images for this contest were drawn from an English data set which is currently being considered in the Tran scriptorium project. The goal of this project is to develop innovative, efficient and cost-effective solutions for the transcription of historical handwritten document images, focusing on four languages: English, Spanish, German and Dutch. For the English language, the so-called "Bentham collection" is being considered in Tran scriptorium. It encompasses a large set of manuscripts written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). A small subset of this collection has been chosen for the present HTR competition. The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant variabilities and difficulties regarding the quality of text images and writing styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts. The three participants achieved very good results, with transcription word error rates ranging from 15.0% down to 8.6%.

关键词： Training Hidden Markov models Histograms Artificial neural networks Text recognition Training data Adaptive optics

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Active learning for interactive neural machine translation of data streams

arXiv

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arXiv 2018年

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent. The user will interactively translate those samples. Once validated, these data is useful for adapting the neural machine translation model. We propose two novel methods for selecting the samples to be validated. We exploit the information from the attention mechanism of a neural machine translation system. Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system. Moreover, it enables to balance the human effort required for achieving a certain translation quality. Moreover, our neural system outperforms classical approaches by a large margin. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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A neural, interactive-predictive system for multimodal sequence to sequence tasks

arXiv

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arXiv 2019年

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks. The system generates text predictions to different sequence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The system reacts to each correction, providing alternative hypotheses, compelling with the feedback provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client–server architecture. For accessing the system, we developed a website, which communicates with the neural model, hosted in a local server. From this website, the different tasks can be tackled following the interactive-predictive framework. We open-source all the code developed for building this system. The demonstration in hosted in http://***/ interactive-seq2seq. Copyright © 2019, The Authors. All rights reserved.

关键词： Websites

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AnnoTheia: A Semi-Automatic Annotation Toolkit for Audio-Visual Speech technologies 30

AnnoTheia: A Semi-Automatic Annotation Toolkit for Audio-Vis...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Acosta-Triana, José M. Gimeno-Gómez, David Martínez-Hinarejos, Carlos D. ValgrAI - Valencian Graduate School and Research Network of Artificial Intelligence Camino de Vera s/n 3Q Building València46022 Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Camino de Vera s/n València46022 Spain

ISBN: (纸本)9782493814104

More than 7,000 known languages are spoken around the world. However, due to the lack of annotated resources, only a small fraction of them are currently covered by speech technologies. Albeit self-supervised speech representations, recent massive speech corpora collections, as well as the organization of challenges, have alleviated this inequality, most studies are mainly benchmarked on English. This situation is aggravated when tasks involving both acoustic and visual speech modalities are addressed. In order to promote research on low-resource languages for audio-visual speech technologies, we present AnnoTheia, a semi-automatic annotation toolkit that detects when a person speaks on the scene and the corresponding transcription. In addition, to show the complete process of preparing AnnoTheia for a language of interest, we also describe the adaptation of a pre-trained model for active speaker detection to Spanish, using a database not initially conceived for this type of task. The AnnoTheia toolkit, tutorials, and pre-trained models are available at https://***/joactr/AnnoTheia/. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Speech recognition

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Online Learning for Neural Machine Translation Post-editing

arXiv

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arXiv 2017年

作者： Peris, Álvaro Cebrián, Luis Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

Neural machine translation has meant a revolution of the field. Nevertheless, post-editing the outputs of the system is mandatory for tasks requiring high translation quality. Post-editing offers a unique opportunity for improving neural machine translation systems, using online learning techniques and treating the post-edited translations as new, fresh training data. We review classical learning methods and propose a new optimization algorithm. We thoroughly compare online learning algorithms in a post-editing scenario. Results show significant improvements in translation quality and effort reduction. Copyright © 2017, The Authors. All rights reserved.

关键词： Learning algorithms

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