Arabic Handwriting Recognition Using Bernoulli HMMs

Hidden Markov Models (HMMs) are now widely used in off-line handwriting recognition and, in particular, in Arabic handwritten word recognition. In contrast to the conventional approach, based on Gaussian mixture HMMs, we have recently proposed to directly fed columns of raw, binary pixels into Bernoulli mixture HMMs. In this work, column bit vectors are extended by means of a sliding window of adequate width to better capture image context at each horizontal position of the word image. Using these windowed Bernoulli mixture HMMs, very good results are reported on the well-known IfN/ENIT database of Arabic handwritten Tunisian town names.

Hidden Markov Models (HMM) are used in handwritten strokes recognition task. The two design parameters of HMM are the number of states and number of mixtures in each state. There are two approaches for finding the number of states, namely, equal number of states and variable number of states. Since the shape of strokes will be different, variable number of states approach should be beneficial. This work proposes a curvature point detection based method to predict variable number of states for modeling a handwritten stroke. The proposed method selects appropriate points from a trace so that the portion between two consecutive points is modeled as an HMM state. Accordingly, based upon handwritten stroke shape complexity, the number of appropriate points selected will change and hence the number of states assigned to the corresponding stroke. In the proposed method, the number of states is proportional to the shape complexity of the given stroke as opposed to fixed in case of brute-force. The HMM based stroke recognizer consisting of 181 distinct strokes, was trained on a set of 52,977 examples collected from approximately 100 native Assamese writers. The evaluation was done on 43,828 examples collected from same users in different sessions. The experimental results demonstrate the benefits of the proposed technique over the brute-force method, especially in case of complex shape strokes.

Hidden Markov Models (HMMs) is one of the sophisticated methods for ASR system. In this work, we propose an Automatic Speech-Speaker Recognition (ASSR) system based on syllable models with HMM for speech expert on a FPGA platform. HMM is used to train the acoustic model for each phoneme evaluated on two databases, i.e. THCHS-30 (Tsinghua Chinese 30 hour database) and CMU ARCTIC Databases. The binary halved clustering (BHC) method which used binary-halved splitting to generate speaker models for low complexity requirement on speaker expert. The experiments are conducted on two types of platforms, including PC and Xilinx Spartan-6 FPGA. The experimental results indicate that the proposed work can achieve 90.8% accuracy rate at Mandarin speech recognition and 86.6% accuracy rate on English speech recognition, respectively. In addition, the work also evaluates OOV (Out of Vocabulary) which reaches 88.7% of detection rate on Mandarin and 84.9% of OOV detection rate on English as well. The speaker recognition rate also reaches to 81.3% and OOS (Out of Speaker) detection rate is 80.8%, respectively.

Hidden Markov models (HMMs) have become within a few years the main technology for online handwritten word recognition (HWR). We consider segment models which generalize HMMs, these models aim at modeling the signal at a global level rather than at the frame level and have been shown to overcome standard HMMs in their modeling ability. We propose a segment model which allows us to automatically handle different writing styles. We compare our system on the isolated character set of the UNIPEN database with a reference system and a baseline segment model.

Hidden Markov models (HMM) have largely demonstrated their usefulness in the fields of statistics and pattern recognition, particularly for speech recognition and hand writing recognition. In the field of genetics, the same principles of statistics and probability can be applied. DNA primarily has four bases: adenine, guanine, thymine, and cytosine, which when paired together can form nucleotides. However, the length of a nucleotide chain can be uncertain. The DNA sequence constitutes the heritable genetic information in nuclei that forms the basis for the developmental programs of all living organisms. Determining the DNA sequence is therefore useful in studying fundamental biological processes, as well as in diagnostic or forensic research. In this study, we will utilize hidden Markov models (HMM) to determine DNA sequence likelihoods. A training sequence of nucleotide bases of the first 1000 bases of rice chromosomes will be used, and the transition and emission probabilities would determine a probable DNA sequence of the next 2000 bases. This sequence should be comparable to the actual sequence. However, experimentation did not show this to be the case, despite previous experiments showing otherwise. Only a fourth of a nucleotide sequence was ever classified correctly.

In this dissertation, a novel approach of integrating template matching with statistical modeling is proposed to improve continuous speech recognition. Hidden Markov Modeling (HMMs) has been the dominant approach in statistical speech recognition since it provides a principled way of jointly modeling speech spectral variations and time dynamics. However, HMMs have the shortcoming of assuming the observations being independent within each state, which makes it ineffective in modeling the details of speech temporal evolutions that are important for characterizing nonstationary speech sounds. Template-based methods make comparisons between a test pattern and the templates derived from training data, and therefore they are able to capture speech dynamics and time correlation of speech frames better than HMM based methods. However, template matching requires large memory space and computational time since feature vectors of training data need to be stored in computer memory for access at the recognition stage, which is difficult in large vocabulary continuous speech recognition (LVCSR). Our proposed approach takes advantages of both statistical modeling and template matching, which overcomes the weakness of conventional template-based method and is feasible for LVCSR. We use multiple Gaussian Mixture Model (GMM) indices to represent each frame of speech templates, and define the template unit to be context-dependent phone segments (triphone context). We also use phonetic decision trees borrowed from those commonly used in HMMs to tie triphone templates and predict triphones unseen in training data. Two local distances, log likelihood ratio (LLR) and Kullback-Leibler (KL) divergence, are proposed for dynamic time warping (DTW) based template matching. In order to reduce computational complexity and storage space, we propose methods of minimum distance template selection (MDTS) and maximum log-likelihood template selection (MLTS), and investigate a template compression method on top of template selection to further improve recognition performance. The template based methods were used to rescore lattices generated by baseline HMMs on the tasks of TIMIT continuous phone recognition and teleheath LVCSR and experimental results demonstrated that the proposed approach of integrating template matching with statistical modeling significantly improved recognition performances over the HMM baselines. The template selection methods also provided significant recognition accuracy improvements over the HMM baseline while largely reducing the computation and storage complexities. When all templates or MDTS were used, using the LLR local distance obtained better recognition performance than the KL divergence local distance. For MLTS and template compression, KL divergence local distance provided better performance than the LLR local distance, and the template compression method made further improvements over KL based MLTS. Since the templates were constructed based on the GMM indices extracted from HMM baselines, we also validated the effectiveness of the proposed template methods based on enhanced HMM baselines. Experimental results showed that LLR based all template method was able to consistently improve TIMIT phone recognition accuracies based on four enhanced HMM baselines. Prosodic features such as duration, energy, and pitch can reflect longer span information of speech than conventional single frame vectors but they have commonly been ignored by HMMs. Template based methods provide possibilities to conveniently integrate prosodic features into speech recognition, which has not been well studied in the past. In this dissertation, we investigate combining template based methods with the speech prosodic features of duration, energy and pitch to further improve speech recognition accuracy. The scores of prosodic information were computed by a GMM based method and a non-parametric method, and the prosodic scores were combined with the acoustic scores in triphone template matching. Experimental results obtained on the telehealth task showed that prosodic information had positive effects on vowel sound recognition.

Hidden Markov models (HMMs) have been widely used in various fields, including image categorization and retrieval. Most of the existing methods train HMMs by low-level features of image blocks; however, the blockbased features can not reflect high-level semantic concepts well. This paper proposes a new method to train HMMs by region-based features, which can be obtained after image segmentation. Our work can be characterized by two key properties: (1) Region-based HMM is adopted to achieve better categorization performance, for the region-based features accord with the human perception better. (2) Multi-layer semantic representation (MSR) is introduced to couple with region-based HMM in a long-term relevance feedback framework for image retrieval. The experimental results demonstrate the effectiveness of our proposal in both aspects of categorization and retrieval.

Hidden Markov models (HMMs) with explicit state duration density (HMM/SD) can represent the time-varying characteristics of speech signals more accurately. However, such an advantage is reduced in relatively smooth state duration densities or long bounded duration. To solve this problem, the authors propose HMMs with global path constraint (HMM/GPC) where the transition between states occur only within prescribed time slots. HMM/GPC explicitly limits state durations and accurately describes the temporal structure of speech simply and efficiently. HMMs formed by combining HMM/GPC with HMM/SD are also presented (HMM/SD+GPC) and performances are compared. HMM/GPC can be implemented with slight modifications to the conventional Viterbi algorithm. HMM/GPC and HMM/SD+GPC not only show superior performance than the conventional HMM and HMM/SD but also require much less computation. >

In this work, we investigate the combination of PGM (Propabilistic Graphical Models) classifiers, either independent or coupled, for the recognition of Arabic handwritten words. The independent classifiers are vertical and horizontal HMMs (Hidden Markov Models) whose observable outputs are features extracted from the image columns and the image rows respectively. The coupled classifiers associate the vertical and horizontal observation streams into a single DBN (Dynamic Bayesian Network). A novel method to extract word baseline and a simple and easily extractable features to construct feature vectors for words in the vocabulary are proposed. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders, loops and diacritic points. Experiments on handwritten Arabic words from IFN/ENIT strongly support the feasibility of the proposed approach. The recognition rates achieve 90.42% with vertical and horizontal HMM, 85.03% and 85.21% with respectively a first and a second DBN which outperform results of some works based on PGMs.

Automatic speech recognition (ASR) has been extensively studied during the past few decades. Today, most of the ASR system based on statistical modelling, and HMM is the most popular one among them. Now days, performance of ASR become one of the major bottleneck for its practical use. Literature of ASR shows that optimal performance could be achieved by using Gaussian mixture hidden Markov model (HMM) but choice of Gaussian mixture is arbitrary with little justification. If we use the different number of Gaussian Mixture then we get different results. In this paper, we compare the performance of continuous Hindi speech recognition by using different number of Gaussian mixture. The aim of this paper is to investigate the optimal number of Gaussian mixture that exhibits maximum accuracy in the context of Hindi speech recognition. HMM toolkit HTK 3.4.1 is used for the implementation of this system, in which Mel frequency cepstral coefficient (MFCC) is used as a feature extraction technique. The experimental results show that the maximum performance of the proposed system is achieved when we use four component Gaussian mixtures HMM model.

Hidden Markov models are a generalization of the finite mixture model that allow for dependence in a sequence of observations via Markovian dependence of an unobserved (hidden) sequence of states. Inference problems associated with hidden Markov models include parameter estimation and restoration of the hidden state sequence. The recursive algorithms employed to solve these problems are related to the Kalman filter and have spawned a wide variety of applications. Areas of application range from speech recognition and signal processing through analysis of molecular sequence data.

Hidden Markov models are now used in many fields, for example, speech recognition, natural language processing etc. However, the mathematical foundation of analysis for the models has not yet been constructed, since the HMMs are non-identifiable. In recent years, we have developed the algebraic geometrical method that allows us to analyze the non-regular and non-identifiable models. In this paper, we apply this method to the HMM and reveal the asymptotic order of its stochastic complexity in the mathematically rigorous way.

Hidden Markov models (HHMs), while well applied in fields such as speech recognition and optical character recognition, have not been used in post-classification for search engines. We explore the use of HMMs for optimization of search engines tasks, specifically focusing on how to construct a new model structure to improve the classification of web pages. We show that a manually constructed new structure model that contains only two states and two classes of observations per field can produce good classification results, and discuss strategies for learning the model structure automatically from data. We also demonstrate that the use of new structure model to classify the search results using some search engines and some different search keywords provide a significant improvement in search accuracy. Our models are applied to the task of post-classifying the web pages selected by the search engine Google, and achieve a classification accuracy of 93.4.

Hidden Markov models (HMMs) are usually learned using the expectation maximisation algorithm which is, unfortunately, subject to local optima. Spectral learning for HMMs provides a unique, optimal solution subject to availability of a sufficient amount of data. However, with access to limited data, there is no means of estimating the accuracy of the solution of a given model. In this paper, a new spectral evaluation method has been proposed which can be used to assess whether the algorithm is converging to a stable solution on a given dataset. The proposed method is designed for real-life datasets where the true model is not available. A number of empirical experiments on synthetic as well as real datasets indicate that our criterion is an accurate proxy to measure quality of models learned using spectral learning.

Hidden Markov Models (HMMs) and associated Markov modulated time series are widely used for estimation and control in e.g. robotics, econometrics and bioinformatics. In this paper, we modify and extend a recently proposed approach in the machine learning literature that uses the method of moments and a Non-Negative Matrix Factorization (NNMF) to estimate the parameters of an HMM. In general, the method aims to solve a constrained non-convex optimization problem. In this paper, it is shown that if the observation probabilities of the HMM are known, then estimating the transition probabilities reduces to a convex optimization problem. Three recursive algorithms are proposed for estimating the transition probabilities of the underlying Markov chain, one of which employs a generalization of the Pythagorean trigonometric identity to recast the problem into a non-constrained optimization problem. Numerical examples are presented to illustrate how these algorithms can track slowly time-varying transition probabilities.