Sensing Analysis of Feature Extraction Types for Handwritten Character Recognition

摘要

For HCCR, the three convNet-based methods obtained the highest accuracies of 94.53, 94.73, and 94.97%, but the accuracies of the other two types, LDD and GD, were 91.93 and 92.66%, respectively. Similarly, for HJCR, the accuracies of the three convNet-based methods were 94.32, 94.89, and 95.11%, but those of LDD and GD were 91.82 and 93.43%, respectively. Therefore, the convNet-based feature extraction is the most robust and discriminating compared with the two traditional methods, LDD and GD, for both HCCR and HJCR. The two convNet-based methods with domain-specific knowledge, convNet-based-LDD and convNet-based-GD, obtained the best accuracies of 94.73 and 94.97% for HCCR and HJCR, respectively. However, the accuracies of convNet-based-Raw without domain-specific knowledge were 94.53 and 94.32% for HCCR and HJCR, respectively. The results demonstrate that domain-specific knowledge makes the convNet-based method more efficient and effective for HCCR and HJCR.