Importance sampling based discriminative learning for large scale offline handwritten Chinese character recognition

• Propose an importance sampling based discriminative learning framework for large scale classification problem.
• Introduce rejection sampling, boosting algorithm and MCE to estimate sample importance weight.
• Compare the methods on large scale character recognition problem and summarize them under the unify framework.

The development of a discriminative learning framework based on importance sampling for large-scale classification tasks is reported in this paper. The framework involves the assignment of samples with different weights according to the sample importance weight function derived from the Bayesian classification rule. Three methods are used to calculate the sample importance weights for learning the modified quadratic discriminant function (MQDF). (1) Rejection sampling method. The method selects important samples as a training subset and trains different levels of MQDFs by focusing on different types of samples. (2) Boosting algorithm. The algorithm modifies the sample importance weights iteratively according to the recognition performance. (3) Minimum classification error (MCE) rule. The parameter of the importance weight function is estimated using the MCE rule. In general, the cursive samples are usually misclassified or prone to be misclassified by the MQDF learned under the maximum likelihood estimation (MLE) rule. The proposed importance sampling framework thereby makes the MQDF classifier focus more on cursive samples than on normal samples. Such a strategy allows the MQDF to achieve higher accuracy while maintaining lower computational complexity. Comprehensive experiments on three Chinese handwritten character datasets demonstrated that the proposed framework exhibits promising character recognition accuracy.