Writer Adaptive Online Handwriting Recognition Using Incremental Linear Discriminant Analysis

Abstract

Writer adaptive handwriting recognition, which has potential of increasing accuracies for a particular user, is the process of converting a writer-independent recognition system to a writer-dependent one. In this paper, we provide a general incremental learning solution for linear discriminant analysis (LDA) on the basis of previous researches, and propose an Incremental LDA (ILDA) based writer adaptive online handwriting recognition method. The adaptation is performed by modifying both the prototypes and the LDA transformation matrix through ILDA algorithm. It includes: (1) modifying prototypes in original feature space; (2) updating the LDA transformation matrix; (3) projecting the updated prototypes to LDA feature space. Experiments are performed on two datasets, the writer-dependent dataset, in which the writing style is consistent with the incremental training data, and the writer-independent dataset. The results demonstrated that our proposed method can reduce as much as 46.35% error rate on the writer-dependent dataset with only 0.20% accuracy loss on the writer-independent dataset. It indicates that our proposed method can significantly increase the recognition accuracy for a particular writer while has minor effects for general writers.