Analytical decision boundary feature extraction for neural networks for the recognition of unconstrained handwritten digits

Although neural networks have been successfully applied for the recognition of unconstrained handwritten characters, there have been few efficient feature extraction algorithms, resulting in inefficient neural networks. We apply a decision boundary feature extraction algorithm to neural networks for the recognition of handwritten digits and reduce the computational cost and complexity of neural networks. Experiments show that the proposed feature extraction algorithm can reduce the number of features significantly without sacrificing the performance