Abstract:

In last years Hidden Markov Models (HMMs) have received significant attention in the
task offline handwritten text recognition (HTR). As in automatic speech recognition (ASR),
HMMs are used to model the probability of ...[+]
In last years Hidden Markov Models (HMMs) have received significant attention in the
task offline handwritten text recognition (HTR). As in automatic speech recognition (ASR),
HMMs are used to model the probability of an observation sequence, given its corresponding
text transcription. However, in contrast to what happens in ASR, in HTR there is no standard
set of local features being used by most of the proposed systems. In this thesis we propose the
use of raw binary pixels as features, in conjunction with models that deal more directly with
the binary data. In particular, we propose the use of Bernoulli HMMs (BHMMs), that is, conventional
HMMs in which Gaussian (mixture) distributions have been replaced by Bernoulli
(mixture) probability functions. The objective is twofold: on the one hand, this allows us
to better modeling the binary nature of text images (foreground/background) using BHMMs.
On the other hand, this guarantees that no discriminative information is filtered out during
feature extraction (most HTR available datasets can be easily binarized without a relevant
loss of information).
In this thesis, all the HMM theory required to develop a HMM based HTR toolkit is
reviewed and adapted to the case of BHMMs. Specifically, we begin by defining a simple
classifier based on BHMMs with Bernoulli probability functions at the states, and we end
with an embedded Bernoulli mixture HMM recognizer for continuous HTR. Regarding the
binary features, we propose a simple binary feature extraction process without significant
loss of information. All input images are scaled and binarized, in order to easily reinterpret
them as sequences of binary feature vectors. Two extensions are proposed to this basic feature
extraction method: the use of a sliding window in order to better capture the context,
and a repositioning method in order to better deal with vertical distortions. Competitive results
were obtained when BHMMs and proposed methods were applied to wellknown HTR
databases. In particular, we ranked first at the Arabic Handwriting Recognition Competition
organized during the 12th International Conference on Frontiers in Handwriting Recognition
(ICFHR 2010), and at the Arabic Recognition Competition: Multifont Multisize Digitally
Represented Text organized during the 11th International Conference on Document Analysis
and Recognition (ICDAR 2011).
In the last part of this thesis we propose a method for training BHMM classifiers using In last years Hidden Markov Models (HMMs) have received significant attention in the
task offline handwritten text recognition (HTR). As in automatic speech recognition (ASR),
HMMs are used to model the probability of an observation sequence, given its corresponding
text transcription. However, in contrast to what happens in ASR, in HTR there is no standard
set of local features being used by most of the proposed systems. In this thesis we propose the
use of raw binary pixels as features, in conjunction with models that deal more directly with
the binary data. In particular, we propose the use of Bernoulli HMMs (BHMMs), that is, conventional
HMMs in which Gaussian (mixture) distributions have been replaced by Bernoulli
(mixture) probability functions. The objective is twofold: on the one hand, this allows us
to better modeling the binary nature of text images (foreground/background) using BHMMs.
On the other hand, this guarantees that no discriminative information is filtered out during
feature extraction (most HTR available datasets can be easily binarized without a relevant
loss of information).
In this thesis, all the HMM theory required to develop a HMM based HTR toolkit is
reviewed and adapted to the case of BHMMs. Specifically, we begin by defining a simple
classifier based on BHMMs with Bernoulli probability functions at the states, and we end
with an embedded Bernoulli mixture HMM recognizer for continuous HTR. Regarding the
binary features, we propose a simple binary feature extraction process without significant
loss of information. All input images are scaled and binarized, in order to easily reinterpret
them as sequences of binary feature vectors. Two extensions are proposed to this basic feature
extraction method: the use of a sliding window in order to better capture the context,
and a repositioning method in order to better deal with vertical distortions. Competitive results
were obtained when BHMMs and proposed methods were applied to wellknown HTR
databases. In particular, we ranked first at the Arabic Handwriting Recognition Competition
organized during the 12th International Conference on Frontiers in Handwriting Recognition
(ICFHR 2010), and at the Arabic Recognition Competition: Multifont Multisize Digitally
Represented Text organized during the 11th International Conference on Document Analysis
and Recognition (ICDAR 2011).
In the last part of this thesis we propose a method for training BHMM classifiers using In last years Hidden Markov Models (HMMs) have received significant attention in the
task offline handwritten text recognition (HTR). As in automatic speech recognition (ASR),
HMMs are used to model the probability of an observation sequence, given its corresponding
text transcription. However, in contrast to what happens in ASR, in HTR there is no standard
set of local features being used by most of the proposed systems. In this thesis we propose the
use of raw binary pixels as features, in conjunction with models that deal more directly with
the binary data. In particular, we propose the use of Bernoulli HMMs (BHMMs), that is, conventional
HMMs in which Gaussian (mixture) distributions have been replaced by Bernoulli
(mixture) probability functions. The objective is twofold: on the one hand, this allows us
to better modeling the binary nature of text images (foreground/background) using BHMMs.
On the other hand, this guarantees that no discriminative information is filtered out during
feature extraction (most HTR available datasets can be easily binarized without a relevant
loss of information).
In this thesis, all the HMM theory required to develop a HMM based HTR toolkit is
reviewed and adapted to the case of BHMMs. Specifically, we begin by defining a simple
classifier based on BHMMs with Bernoulli probability functions at the states, and we end
with an embedded Bernoulli mixture HMM recognizer for continuous HTR. Regarding the
binary features, we propose a simple binary feature extraction process without significant
loss of information. All input images are scaled and binarized, in order to easily reinterpret
them as sequences of binary feature vectors. Two extensions are proposed to this basic feature
extraction method: the use of a sliding window in order to better capture the context,
and a repositioning method in order to better deal with vertical distortions. Competitive results
were obtained when BHMMs and proposed methods were applied to wellknown HTR
databases. In particular, we ranked first at the Arabic Handwriting Recognition Competition
organized during the 12th International Conference on Frontiers in Handwriting Recognition
(ICFHR 2010), and at the Arabic Recognition Competition: Multifont Multisize Digitally
Represented Text organized during the 11th International Conference on Document Analysis
and Recognition (ICDAR 2011).
In the last part of this thesis we propose a method for training BHMM classifiers using discriminative training criteria, instead of the conventionalMaximum Likelihood Estimation
(MLE). Specifically, we propose a loglinear classifier for binary data based on the BHMM
classifier. Parameter estimation of this model can be carried out using discriminative training
criteria for loglinear models. In particular, we show the formulae for several MMI based
criteria. Finally, we prove the equivalence between both classifiers, hence, discriminative
training of a BHMM classifier can be carried out by obtaining its equivalent loglinear classifier.
Reported results show that discriminative BHMMs clearly outperform conventional
generative BHMMs.
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