Unconstrained Handwritten Character Recognition Using Different Classification Strategies

/in , , /by
Unconstrained Handwritten Character Recognition Using Different Classification Strategies

Koerich, Alessandro L

International Workshop on Artificial Neural Networks łdots 2003

Abstract : In this paper we tackle the problem of unconstrained handwritten character recognition using different classification strategies. For such an aim, four multilayer perceptron classifiers (MLP) are built and used into three different classification strategies: combination łdots

You might also like
Ventilation control strategy using supply CO2 concentration setpoint
Local discriminative characterization of MRI for Alzheimer’s disease
Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology
Deep learning forecasts the occurrence of sleep apnea from single-lead ECG
A convex max-flow segmentation of LV using subject-specific distributions on cardiac MRI
Pixel level image fusion for medical imaging: an energy minimizing approach
Forest species recognition based on ensembles of classifiers
Técnicas em Processamento e Análise de Documentos Manuscritos