Bernoulli HMMs at subword level for handwritten word recognition
This paper presents a handwritten word recogniser based on HMMs at subword level (characters) in which state-emission probabilities are governed by multivariate Bernoulli probability functions. This recogniser works directly with raw binary pixels of the image, instead of conventional, real-valued l...
| Autores: | , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/51259 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/51259 |
| Access Level: | acceso abierto |
| Palabra clave: | HMM Subword Bernoulli Handwritten word recognition LENGUAJES Y SISTEMAS INFORMATICOS |
| Sumario: | This paper presents a handwritten word recogniser based on HMMs at subword level (characters) in which state-emission probabilities are governed by multivariate Bernoulli probability functions. This recogniser works directly with raw binary pixels of the image, instead of conventional, real-valued local features. A detailed experimentation has been carried out by varying the number of states, and comparing the results with those from a conventional system based on continuous (Gaussian) densities. From this experimentation, it becomes clear that the proposed recogniser is much better than the conventional system |
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