A Modified Inception-ResNet Network with Discriminant Weighting Loss for Handwritten Chinese Character Recognition

摘要

Handwritten Chinese character recognition (HCCR) is a representative large character set pattern classification task. Recently, convolutional neural networks have provided promising solutions for this challenging task. This paper adopts the modified Inception-ResNet network for handwritten Chinese character recognition, and proposes a discriminant weighting method for cross-entropy loss calculation which focuses on recognition errors in the training stage. Sparse training technique is also incorporated. Under the specific condition of utilizing the testing mini-batch mean and variance for batch normalization, the proposed method achieves improved performance on the ICDAR-2013 offline handwritten Chinese character competition dataset.