Markov Models for Handwriting Recognition [recurso electrónico] / by Thomas Plötz, Gernot A. Fink.
Tipo de material: TextoSeries SpringerBriefs in Computer ScienceEditor: London : Springer London, 2011Descripción: VI, 78p. 5 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781447121886Tema(s): Computer science | Optical pattern recognition | Computer Science | Pattern RecognitionFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006.4 Clasificación LoC:Q337.5TK7882.P3Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
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Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | Q337.5 (Browse shelf(Abre debajo)) | 1 | No para préstamo | 372353-2001 |
Introduction -- General Architecture -- Markov Model Concepts: The Essence -- Markov Model Based Handwriting Recognition -- Recognition Systems for Practical Applications -- Discussion.
Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. Markov Models for Handwriting Recognition provides a comprehensive overview of the application of Markov models in the research field of handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, the text introduces the typical architecture of a Markov model-based handwriting recognition system, and familiarizes the reader with the essential theoretical concepts behind Markovian models. Then, the text gives a thorough review of the solutions proposed in the literature for open problems in applying Markov model-based approaches to automatic handwriting recognition.
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