GPU-Based Fast Training of Discriminative Learning Quadratic Discriminant Function for Handwritten Chinese Character Recognition

The discriminative training of classifiers for handwritten Chinese character recognition (HCCR) is highly demanding in computation due to the large number of categories. The inability of discriminative training with large sample set on personal computers has hindered the accuracy promotion for HCCR. To overcome this problem, we have implemented the training algorithm of discriminative learning quadratic discriminant function (DLQDF) on our graphics processing units (GPU) server, and have achieved 15 times speedup compared to single-core computation. By enlarging training sample set via distortion on a standard dataset of 3,755 classes, we could train the DLQDF on more than 50 million samples within 150min and get the test accuracy improved by 1.36%.