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International Workshop on Frontiers in Handwriting recognition

作者： Gundram Leifert Roger Labahn Joan Andreu Sánchez Computational Intelligence Technology Lab University of Rostock Rostock Germany Pattern Recognition and Human Language Technologies Center Universitat Politécnica de València València Spain

ISBN: (数字)9781728199665

ISBN: (纸本)9781728199672

Automated text recognition is a fundamental problem in Document Image Analysis. Optical models are used for modeling characters while language models are used for composing sentences. Since the scripts and linguistic context differ widely, it is mandatory to specialize the models by training on task-dependent ground-truth. However, to create a sufficient amount of ground-truth, at least for historical handwritten scripts, well-qualified persons have to mark and transcribe text lines, which is very time-consuming. On the other hand, in many cases unassigned transcripts are already available on page-level from another process chain, or at least transcripts from similar linguistic context are available. In this work we present two approaches that make use of such transcripts: whereas the first one creates training data by automatically assigning page-dependent transcripts to text lines, the second one uses a task-specific language model to generate highly confident training data. Both approaches are successfully applied on a very challenging historical handwritten collection.

关键词： Training Training data Text recognition Layout Task analysis Production Linguistics

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Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model

arXiv

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arXiv 2022年

作者： Daheim, Nico Thulke, David Dugast, Christian Ney, Hermann Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany Human Language Technology and Pattern Recognition RWTH Aachen University Germany AppTek GmbH Germany

In this work, we present a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes' theorem. One component is a traditional ungrounded response generation model and the other component models the reconstruction of the grounding document based on the dialog context and generated response. We propose different approximate decoding schemes and evaluate our approach on multiple open-domain and task-oriented document-grounded dialog datasets. Our experiments show that the model is more factual in terms of automatic factuality metrics than the baseline model. Furthermore, we outline how introducing scaling factors between the components allows for controlling the tradeoff between factuality and fluency in the model output. Finally, we compare our approach to a recently proposed method to control factuality in grounded dialog, CTRL (Rashkin et al., 2021), and show that both approaches can be combined to achieve additional improvements. © 2022, CC BY.

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HTR-Flor: A Deep Learning System for Offline Handwritten Text recognition

HTR-Flor: A Deep Learning System for Offline Handwritten Tex...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Arthur Flor de Sousa Neto Byron Leite Dantas Bezerra Alejandro Héctor Toselli Estanislau Baptista Lima Escola Politécnica de Pernambuco Universidade de Pernambuco Recife Brazil Pattern Recognition and Human Language Technology Universitat Politècnica de València València Spain

ISBN: (数字)9781728192741

ISBN: (纸本)9781728192758

In recent years, Handwritten Text recognition (HTR) has captured a lot of attention among the researchers of the computer vision community. Current state-of-the-art approaches for offline HTR are based on Convolutional Recurrent Neural Networks (CRNNs) excel at scene text recognition. Unfortunately, deep models such as CRNNs, Recur-rent Neural Networks (RNNs) are likely to suffer from vanishing/exploding gradient problems when processing long text images, which are commonly found in scanned documents. Besides, they usually have millions of parameters which require huge amount of data, and computational resource. Recently, a new class of neural net-work architecture, called Gated Convolutional Neural Networks (Gated-CNN), has demonstrated potentials to complement CRNN methods in modeling. Therefore, in this paper, we present a new architecture for HTR, based on Gated-CNN, with fewer parameters and fewer layers, which is able to outperform the current state-of-the-art architectures for HTR. The experiment validates that the proposed model has statistically significant recognition results, surpassing previous HTR systems by an average of 33% over five important handwritten benchmark datasets. Moreover, the proposed model is able to achieve satisfactory recognition rates even in case of few training data. Finally, its compact architecture requires less computational resources, which can be applied for real-world applications that have hardware limitations, such as robots and smartphones.

关键词： Logic gates Convolution Computer architecture Text recognition Hidden Markov models Optical imaging Computational modeling

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Sample drop detection for distant-speech recognition with asynchronous devices distributed in space

arXiv

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arXiv 2019年

作者： Raissi, Tina Pascual, Santiago Omologo, Maurizio Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Universitat Politècnica de Catalunya Barcelona Spain Center for Information and Communication Technology Fondazione Bruno Kessler Trento Italy

In many applications of multi-microphone multi-device processing, the synchronization among different input channels can be affected by the lack of a common clock and isolated drops of samples. In this work, we address the issue of sample drop detection in the context of a conversational speech scenario, recorded by a set of microphones distributed in space. The goal is to design a neural-based model that given a short window in the time domain, detects whether one or more devices have been subjected to a sample drop event. The candidate time windows are selected from a set of large time intervals, possibly including a sample drop, and by using a preprocessing step. The latter is based on the application of normalized cross-correlation between signals acquired by different devices. The architecture of the neural network relies on a CNN-LSTM encoder, followed by multi-head attention. The experiments are conducted using both artificial and real data. Our proposed approach obtained F1 score of 88% on an evaluation set extracted from the CHiME-5 corpus. A comparable performance was found in a larger set of experiments conducted on a set of multi-channel artificial scenes. Copyright © 2019, The Authors. All rights reserved.

关键词： Speech recognition

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Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks

arXiv

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arXiv 2019年

作者： Maroñas, Juan Paredes, Roberto Ramos, Daniel PRHLT - Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de Valencia Spain AUDIAS - Audio Data Intelligence and Speech Universidad Autónoma de Madrid Spain

Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks. However, recent works have pointed out that the outputs provided by these models are not well-calibrated, seriously limiting their use in critical decision scenarios. In this work, we propose to use a decoupled Bayesian stage, implemented with a Bayesian Neural Network (BNN), to map the uncalibrated probabilities provided by a DNN to calibrated ones, consistently improving calibration. Our results evidence that incorporating uncertainty provides more reliable probabilistic models, a critical condition for achieving good calibration. We report a generous collection of experimental results using high-accuracy DNNs in standardized image classification benchmarks, showing the good performance, flexibility and robust behavior of our approach with respect to several state-of-the-art calibration methods. Code for reproducibility is provided. Copyright © 2019, The Authors. All rights reserved.

关键词： Calibration

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ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 2016)

ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 20...

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International Workshop on Frontiers in Handwriting recognition

作者： Ioannis Pratikakis Konstantinos Zagoris Basilis Gatos Joan Puigcerver Alejandro H. Toselli Enrique Vidal Visual Computing Group Democritus University of Thrace Xanthi Greece Institute of Informatics and Telecommunications National Center for Scientific Research “Demokritos” Athens Greece Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

ISBN: (纸本)9781509009824

The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up an evaluation framework for benchmarking handwritten keyword spotting (KWS) examining both the Query by Example (QbE) and the Query by String (QbS) approaches. Both KWS approaches were hosted into two different tracks, which in turn were split into two distinct challenges, namely, a segmentation-based and a segmentation-free to accommodate different perspectives adopted by researchers in the KWS field. In addition, the competition aims to evaluate the submitted training-based methods under different amounts of training data. Four participants submitted at least one solution to one of the challenges, according to the capabilities and/or restrictions of their systems. The data used in the competition consisted of historical German and English documents with their own characteristics and complexities. This paper presents the details of the competition, including the data, evaluation metrics and results of the best run of each participating methods.

关键词： Image segmentation Training data Training Measurement Histograms Handwriting recognition Visualization

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On the Benefits of Convolutional Neural Network Combinations in Offline Handwriting recognition

On the Benefits of Convolutional Neural Network Combinations...

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International Workshop on Frontiers in Handwriting recognition

作者： Dewi Suryani Patrick Doetsch Hermann Ney King Mongkut's University of Technology North Bangkok The Sirindhorn International Thai-German Graduate School of Engineering Bangkok Thailand Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781509009824

In this paper, we elaborate the advantages of combining two neural network methodologies, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent neural networks, with the framework of hybrid hidden Markov models (HMM) for recognizing offline handwriting text. CNNs employ shift-invariant filters to generate discriminative features within neural networks. We show that CNNs are powerful tools to extract general purpose features that even work well for unknown classes. We evaluate our system on a Chinese handwritten text database and provide a GPU-based implementation that can be used to reproduce the experiments. All experiments were conducted with RWTH OCR, an open-source system developed at our institute.

关键词： Hidden Markov models Neurons Training Handwriting recognition Biological neural networks Computer architecture Logic gates

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Mobile music modeling, analysis and recognition

Mobile music modeling, analysis and recognition

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Pavel Golik Boulos Harb Ananya Misra Michael Riley Alex Rudnick Eugene Weinstein Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Google Inc. New York NY USA School of Informatics and Computing Indiana University Bloomington IN USA

We present an analysis of music modeling and recognition techniques in the context of mobile music matching, substantially improving on the techniques presented in [1]. We accomplish this by adapting the features specifically to this task, and by introducing new modeling techniques that enable using a corpus of noisy and channel-distorted data to improve mobile music recognition quality. We report the results of an extensive empirical investigation of the system's robustness under realistic channel effects and distortions. We show an improvement of recognition accuracy by explicit duration modeling of music phonemes and by integrating the expected noise environment into the training process. Finally, we propose the use of frame-to-phoneme alignment for high-level structure analysis of polyphonic music.

关键词： Training Accuracy Hidden Markov models Music Speech recognition USA Councils

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Influence of text line segmentation in Handwritten Text recognition

Influence of text line segmentation in Handwritten Text Reco...

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International Conference on Document Analysis and recognition

作者： Verónica Romero Joan Andreu Sánchez Vicente Bosch Katrien Depuydt Jesse de Does Pattern Recognition and Human Language Technology Universitat Politècnica de València Camí de Vera Valencia Spain Instituut voor Nederlandse Lexicologie 2300RA Matthias de Vrieshof 2–3 Leiden Netherlands

Text line segmentation is the process by which text lines in a document image are localized and extracted. It is an important step in off-line Handwritten Text recognition (HTR) given that the input of these systems is the line image of the text to be transcribed. A myriad of solutions to the text line segmentation problem have been proposed in the literature. Although these solutions may differ greatly on what is actually applied to perform the segmentation, they can be classified by the level of precision and detail in the final extracted lines. In this paper we study the influence and real needs of different levels of precision and detail in the segmentation solutions in a real HTR task. We test three technics of text line segmentation whose output range from a simple rectangle for each line to a perfect fitted polygon surrounding the detected lines. Experiments have been carried out with a historical collection and results show that good HTR accuracy can be obtained with simple extraction algorithms.

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Automatic Semantic Segmentation of Structural Elements related to the Spinal Cord in the Lumbar Region by using Convolutional Neural Networks

Automatic Semantic Segmentation of Structural Elements relat...

引用

International Conference on pattern recognition

作者： Jhon Jairo Saenz-Gamboa Maria de la Iglesia-Vayá Jon A. Gómez 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

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. 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.

关键词： Image segmentation Semantics Spine Morphology Computer architecture Blood vessels Fats

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