In this paper, Hidden Markov Models (HMMs) are investigated for the purpose of classifying planar shapes represented by their curvature coefficients. In the training phase, special attention is devoted to the initialization and model selection issues, which make the learning phase particularly effective. The results of tests on different data sets show that the proposed system is able to accurately classify objects that were translated, rotated, occluded, or deformed by shearing, also in the presence of noise.

Clustering of sequential or temporal data is more challenging than traditional clustering as dynamic observations should be processed rather than static measures. This paper proposes a Hidden Markov Model (HMM)-based technique suitable for clustering of data sequences. The main aspect of the work is the use of a probabilistic model-based approach using HMM to derive new proximity distances, in the likelihood sense, between sequences. Moreover, a novel partitional clustering algorithm is designed which alleviates computational burden characterizing traditional hierarchical agglomerative approaches. Experimental results show that this approach provides an accurate clustering partition and the devised distance measures achieve good performance rates. The method is demonstrated on real world data sequences, i.e. the EEG signals due to their temporal complexity and the growing interest in the emerging field of Brain Computer Interfaces.

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The field of personal computing has begun to make a transition from the desktop to handheld devices, thereby requiring input paradigms that are more suited for single hand entry than a keyboard and recent developments in online handwriting recognition allow for such input modalities. Data entry using a pen forms a natural, convenient interface. The large number of writing styles and the variability between them makes the problem of writer-independent unconstrained handwriting recognition a very challenging pattern recognition problem. The state-of-the-art in online handwriting recognition is such that it has found practical success in very constrained problems. In this thesis, a method of identifying different writing styles, referred to as lexemes, is described. Approaches for constructing both non-parametric and parametric classifiers are described that take advantage of the identified lexemes to f...

Contents 1 Background 4 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Types of handwriting recognition systems . . . . . . . . . . . . . . . . 5 1.2.1 Ooe-line recognition . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.2 On-line recognition . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.3 Character sets . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.4 Writing style variations . . . . . . . . . . . . . . . . . . . . . . 7 1.2.4.1 Variations of characters . . . . . . . . . . . . . . . . 7 1.2.4.2 Alignment of characters . . . . . . . . . . . . . . . . 8 1.2.4.3 Personal background factors . . . . . . . . . . . . . . 8 1.2.4.4 Situational factors . . . . . . . . . . . . . . . . . . . 9 1.2.4.5 Material factors . . . . . . . . . . . . . . . . . . . . . 9 1.2.4.6 Constraints on writing . . . . . . . . . . . . . . . . . 9 1.2.5 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3 Beneøt

This paper describes methods for document image classification at the spatial layout level. The goal is to develop fast algorithms for initial document type classification without OCR, which can then be verified using more elaborate methods based on more detailed geometric and syntactic models. A novel feature set called interval encoding is introduced to capture elements of spatial layout. This feature set encodes region layout information in fixed-length vectors by capturing structural characteristics of the image. We demonstrate the usefulness of these features derived from interval coding, in a hidden Markov model based page layout classification system that is trainable and extendible.

. The trade-off between high-level, long-range features and low level, local features is common among many pattern recognition problems: the former are usually more powerful but less robust, while the latter is less informative but more reliable. In this paper we describe a new method for combining high-level long-range features and local features for on-line handwriting recognition. First, high-level features such as crossings, loops and cusps are extracted. A localization procedure is then applied to spread these high-level features over the neighboring sample points, resulting in local representations of nearby high-level features. These features are then combined with the usual local features at each sample point and used in an integrated segmentation and recognition process. This method allows incorporation of information carried by high-level long-range features while at the same time maintains the high reliability of the recognition system. We report experimental re...

In this paper, a learning-based paradigm for scientific chart recognition is proposed. Two kinds of chart recognition methods are presented: hidden Markov model based and neural network based method. A newly developed feature extraction method is also put forward for chart images. Experiments on three kinds of charts show that the ergodic hidden Markov models achieve a satisfactory result for chart recognition. Unlike traditional primitive-based diagram recognition method, learningbased approach need not recognize the graphic primitives in charts. Thus the method bypasses the recognition error problem caused by inaccurate primitive extraction that is also a major obstacle to the construction of a general chart recognition system. 1

In this paper, we propose a model-based method to analyze the human walking motion. This system consists of three phases: the preprocessing phase, the model construction phase, and the motion analysis phase. In the experimental results, we show that our system not only analyzes the motion characteristics of the human body, but also recognizes the motion type of the input image sequences. Finally, the synthesized motion sequences are illustrated for verification. The major contributions of this research are: (1) developing a skeleton-based method to analyze the human motion; (2) using Hidden Markov Model (HMM) and posture patterns to describe the motion type. � 2000

this paper we discuss an on-line handwriting recognition system that employs signal preprocessing, feature invariance, and stochastic modeling with HMM's that incorporate a language model. Results are presented for the UNIPEN data