Local Discriminant Training and Global Optimization for Convolutional Neural Network Based Handwritten Chinese Character Recognition

This paper investigates local discriminant training and global optimization methods for Convolutional Neural Network (CNN) to improve its discriminant ability and recognition accuracy. For local discriminant training, we propose to combine triplet loss and softmax with cross-entropy loss as the loss function. The triplet loss is incorporated into an additional fully-connected layer before the final fully-connected layer of a CNN model. For global optimization, we use Conditional Random Field (CRF) to further utilize the pairwise distance of the CNN feature vectors trained with triplet loss. Experiments with different CNN models on handwritten Chinese character samples show that the combined local discriminant training and global optimization scheme achieves better character recognition accuracy and confidence analysis performance.

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