An important step in fingerprint recognition is segmentation. During segmentation the fingerprint image is decomposed into foreground, background and low-quality regions. The foreground is used in the recognition process, the background is ignored. The low-quality regions may or may not be used, dependent on the recognition method. Pixel features of the gray-scale image form the basis of segmen...

In this paper we propose a segmentation system for unconstrained Arabic online handwriting. An essential problem addressed by analytical-based word recognition system. The system is composed of two-stages the first is a newly special designed hidden Markov model (HMM) and the second is a rules based stage. In our system, handwritten words are broken up into characters by simultaneous segmentati...

This paper presents the IfN’s Offline Handwritten Arabic Word Recognition System. The system uses Hidden Markov Models (HMM) for word recognition, and is based on character recognition without explicit segmentation. The first part of this paper deals with databases for word recognition systems, and in particular, the IFN/ENIT database. The second part gives a short description of the pre-proces...

In this article we propose a modification to the HMRF-EM framework applied to image segmentation. To do so, we introduce a new model for the neighborhood energy function of the Hidden Markov Random Fields model based on the Hidden Markov Model formalism. With this new energy model, we aim at (1) avoiding the use of a key parameter chosen empirically on which the results of the current models ar...

ÐThis paper describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentationsymbols, which are both explicitly model...

This paper deals with the problems of automatic segmentation for the purposes of Czech concatenative speech synthesis. Statistical approach to speech segmentation using hidden Markov models (HMMs) is applied in the baseline system. Several improvements of this system are then proposed to get more accurate segmentation results. These enhancements mainly concern the various strategies of HMM init...

Hidden Markov fields (HMF) models are widely applied to various problems arising in image processing. In these models, the hidden process of interest X is a Markov field and must be estimated from its observable noisy version Y. The success of HMF is mainly due to the fact that the conditional probability distribution of the hidden process with respect to the observed one remains Markovian, whi...

A novel Ergodic Multigram Hidden Markov Model (HMM) is introduced which models sentence production as a doubly stochastic process, in which word classes are first produced according to a first order Markov model, and then single or multi-character words are generated independently based on the word classes, without word boundary marked on the sentence. This model can be applied to languages wit...

We are dealing in this paper with audio segmentation. We propose a two level segmentation process that enables the audio tracks to be sampled in short sequences which are classified into several classes. The segmentation is performed by computing several features for each audio sequence. These features are computed either on a complete audio segment or on a frame (set of samples) which is a sub...