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Rejecting Character Recognition Errors Using CNN Based confidence estimation

Chinese Journal of Electronics 2016年第3期25卷 520-526页

作者： LI Pengchao PENG Liangrui WEN Juan Tsinghua National Laboratory for Information Science and Technology Dept.of Electronic EngineeringTsinghua University Equipment Academy

Although Optical character recognition(OCR) technology has achieved huge progress in recent years, character misrecognition is inevitable. In order to realize high fidelity content of document digitalization,we propose a new Convolutional neural networks(CNN)based confidence estimation method. We detect the misrecognized characters through comparing the confidence value with a preset threshold, so as to leave the recognition errors as embedded images in the output digital documents. We adopted sofmax as the estimation of posteriori probability,overlap pooling and maxout with dropout technologies in CNN architecture design. Experimental results show that our method has achieved an explicit improvement compared to baseline system.

关键词： Optical character recognition(OCR) confidence estimation Convolutional neural networks(CNN)

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A novel confidence estimation method for neural networks in multispectral image classification

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International Journal of Digital Earth 2009年第4期2卷 343-358页

作者： O.Arslan Geodesy and Photogrammetry Engineering Department Engineering FacultyUniversity of Kocaeli41040Turkey

The Digital Earth concept has attracted much attention recently and this approach uses a variety of earth observation data from the global to the local *** techniques have made much progress technically and the methods used for automatic extraction of geo-ralated information are of importance in Digital Earth *** of these methods,artificial neural networks(ANN)techniques,have been effectively used in classification of remotely sensed *** image classification with ANN has been producing higher or equal mapping accuracies than parametric *** studies have,in fact,shown that there is no discernible difference in classification accuracies between neural and conventional statistical *** well designed and trained neural networks can present a better performance than the standard statistical *** are,as yet,no widely recognised standard methods to implement an optimum *** this point of view it might be beneficial to quantify ANN’s reliability in classification *** measure the reliability of the neural network might be a way of developing to determine suitable network *** date,the problem of confidence estimation of ANN has not been studied in remote sensing studies.A statistical method for quantifying the reliability of a neural network that can be used in image classification is investigated in this *** this purpose the method is to be based on a binomial experimentation concept to establish confidence *** novel method can also be used for the selection of an appropriate network structure for the classification of multispectral *** the main focus of the research is to estimate confidence in ANN,the approach might also be applicable and relevant to Digital Earth technologies.

关键词： digital earth neural networks confidence estimation image classification

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Term-Dependent confidence Normalisation for Out-of-Vocabulary Spoken Term Detection

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Journal of Computer Science & Technology 2012年第2期27卷 358-375页

作者： Javier Tejedo Simon King Joe Frankel Centre for Speech Technology Research University of Edinburgh10 Crichton Street Human Computer Technology Laboratory (HCTLab) School of Computer Engineering and Telecommunication University Autonomous of MadridAvenue Francisco Tomás y Valiente 1128049MadridSpain

An important component of a spoken term detection （STD） system involves estimating confidence measures of hypothesised detections.A potential problem of the widely used lattice-based confidence estimation,however,is that the confidence scores are treated uniformly for all search terms,regardless of how much they may differ in terms of phonetic or linguistic *** problem is particularly evident for out-of-vocabulary （OOV） terms which tend to exhibit high intra-term *** address the impact of term diversity on confidence measures,we propose in this work a term-dependent normalisation technique which compensates for term diversity in confidence *** first derive an evaluation-metric-oriented normalisation that optimises the evaluation metric by compensating for the diverse occurrence rates among terms,and then propose a linear bias compensation and a discriminative compensation to deal with the bias problem that is inherent in lattice-based confidence measurement and from which the Term Specific Threshold （TST） approach *** tested the proposed technique on speech data from the multi-party meeting domain with two state-ofthe-art STD systems based on phonemes and words *** experimental results demonstrate that the confidence normalisation approach leads to a significant performance improvement in STD,particularly for OOV terms with phonemebased systems.

关键词： confidence estimation discriminative model spoken term detection speech recognition

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