Obtención de las n mejores alternativas para clasificación de símbolos unicode
The Unicode character set has been increased in last years until grouping more than 100000 characters. We developed a classifier which can predict the n most probable solutions to a given handwritten character in a smaller Unicode set. Even with the size reduction we still have a classification prob...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2017 |
| 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:10251/86238 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/86238 |
| Access Level: | acceso abierto |
| Palabra clave: | Data generation Neural network Character recognition Unicode Reconocimiento de caracteres Redes neuronales Generación de datos LENGUAJES Y SISTEMAS INFORMATICOS Máster Universitario en Inteligencia Artificial, Reconocimiento de Formas e Imagen Digital-Màster Universitari en Intel·ligència Artificial, Reconeixement de Formes i Imatge Digital |
| Sumario: | The Unicode character set has been increased in last years until grouping more than 100000 characters. We developed a classifier which can predict the n most probable solutions to a given handwritten character in a smaller Unicode set. Even with the size reduction we still have a classification problem with a big number of classes (5488 in total) without any training sample. Before dealing with this problem we performed some experiments on the UJI PEN dataset. In these experiments we used two different data generation techniques, distortions and variational autoencoders as generative models. We tried feature extraction methods with both offline and online data. The generation along with the feature extraction was tested in several models of neural networks like convolutional networks or LSTM. |
|---|