Bagging by design for continuous Handwriting Recognition using multi-objective particle swarm optimization

Multiple classifier systems are used to improve baseline results using different strategies. Bagging by design improves standard bagging by the minimization of intersection between the different ensembles. This work proposes the use of design bagging for continuous handwriting recognition. The design is performed using a multi-objective particle swarm optimizer. Hidden Markov Models and Long-Short Term Memory Recurrent Neural Networks are used to validate the proposed design. Experiments on English and French Handwriting Recognition with different setups show significant improvements.