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Article
Non-local spatially varying finite mixture models for image segmentation
In this work, we propose a new Bayesian model for unsupervised image segmentation based on a combination of the spatially varying finite mixture models (SVFMMs) and the non-local means (NLM) framework. The pro...
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Chapter and Conference Paper
Efficient Generation of High-Quality Multilingual Subtitles for Video Lecture Repositories
Video lectures are a valuable educational tool in higher education to support or replace face-to-face lectures in active learning strategies. In 2007 the Universitat Politècnica de València (UPV) implemented i...
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Article
Interactive handwriting recognition with limited user effort
Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. Although post-editing automatic recognition of handwritten text is feasible, it is not clearly b...
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Chapter and Conference Paper
Arabic Printed Word Recognition Using Windowed Bernoulli HMMs
Hidden Markov Models (HMMs) are now widely used for off-line text recognition in many languages and, in particular, Arabic. In previous work, we proposed to directly use columns of raw, binary image pixels, wh...
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Chapter and Conference Paper
Character-Based Handwritten Text Recognition of Multilingual Documents
An effective approach to transcribe handwritten text documents is to follow a sequential interactive approach. During the supervision phase, user corrections are incorporated into the system through an ongoing...
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Chapter and Conference Paper
Language Technology for Handwritten Text Recognition
This paper shows how the nowadays prevalent technology used in HTR borrows concepts and methods from the field of ASR; i.e. those based on Hidden Markov Models (HMMs). Additionally, it will be described a HTR ...
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Chapter
Arabic Handwriting Recognition Using Bernoulli HMMs
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...
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Chapter and Conference Paper
Integrating a State-of-the-Art ASR System into the Opencast Matterhorn Platform
In this paper we present the integration of a state-of-the-art ASR system into the Opencast Matterhorn platform, a free, open-source platform to support the management of educational audio and video content. T...
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Chapter and Conference Paper
Language Identification for Interactive Handwriting Transcription of Multilingual Documents
An effective approach to handwriting transcription of (old) documents is to follow a sequential, line-by-line transcription of the whole document, in which a continuously retrained system interacts with the us...
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Article
Constrained domain maximum likelihood estimation for naive Bayes text classification
The naive Bayes assumption in text classification has the advantage of greatly simplifying maximum likelihood estimation of unknown class-conditional word occurrence probabilities. However, these estimates are...
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Chapter and Conference Paper
Bernoulli HMMs at Subword Level for Handwritten Word Recognition
This paper presents a handwritten word recogniser based on HMMs at subword level (characters) in which state-emission probabilities are governed by multivariate Bernoulli probability functions. This recogniser...
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Chapter and Conference Paper
Embedded Bernoulli Mixture HMMs for Continuous Handwritten Text Recognition
Hidden Markov Models (HMMs) are now widely used in off-line handwritten text recognition. As in speech recognition, they are usually built from shared, embedded HMMs at symbol level, in which state-conditional...
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Chapter and Conference Paper
Confidence Measures for Error Correction in Interactive Transcription Handwritten Text
An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system ...
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Article
Iterative Contextual Recurrent Classification of Chromosomes
Recurrent connectionist models provide a method to represent dynamic patterns in a neural network. In this work we present a method for chromosome classification based on an almost unexplored neural network te...
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Chapter and Conference Paper
Inference of Stochastic Finite-State Transducers Using N-Gram Mixtures
Statistical pattern recognition has proved to be an interesting framework for machine translation, and stochastic finite-state transducers are adequate models in many language processing areas such as speech t...
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Chapter and Conference Paper
Explicit Modelling of Invariances in Bernoulli Mixtures for Binary Images
Bernoulli mixture models have been recently proposed as simple yet powerful probabilistic models for binary images in which each image pattern is modelled by a different Bernoulli prototype (component). A poss...
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Chapter and Conference Paper
Using Query-Relevant Documents Pairs for Cross-Lingual Information Retrieval
The world wide web is a natural setting for cross-lingual information retrieval. The European Union is a typical example of a multilingual scenario, where multiple users have to deal with information published...
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Chapter and Conference Paper
Different Approaches to Bilingual Text Classification Based on Grammatical Inference Techniques
Bilingual documentation has become a common phenomenon in many official institutions and private companies. In this scenario, the categorization of bilingual text is a useful tool, that can be also applied in ...
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Chapter and Conference Paper
Word Translation Disambiguation Using Multinomial Classifiers
This work focuses on a hybrid machine translation system from Spanish into Catalan called SisHiTra. In particular, we focus on its word translation disambiguation module, which has to decide on the correct tra...
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Chapter and Conference Paper
The MORFO3D Foot Database
A foot database comprising 3D foot shapes and footwear fitting reports of more than 300 participants is presented. It was primarily acquired to study footwear fitting, though it can also be used to analyse ana...
