Imaginary stroke technique has been proved to be an effective solution to the problem of the stroke connection in online handwritten character recognition. However, it may cause confusions among characters with similar but actually different trajectories after adding imaginary strokes. In this paper, we first investigate both the benefit and the defect of the imaginary stroke technique, and then two modified methods are proposed under the framework of feature fusion and local feature enhance respectively. With the proposed methods, the feature of imaginary strokes is employed to unify the writing styles and the feature of real strokes is enhanced to strengthen discriminability. Experimental results for handwritten Chinese character recognition indicate that comparing with feature without imaginary strokes and feature with imaginary strokes, our proposed methods provide about 3%~8% and 1%~4% recognition accuracy improvement respectively.

Most online handwriting word recognition (HWR) approaches proceed by segmenting words into isolate characters which are recognized separately. Inspired by results in cognitive psychology, holistic word recognition approaches provides another effective way to deal the problem of HWR. In this paper, we propose a new method for rotation free online unconstrained Chinese word recognition through a holistic approach. By a gravity center balancing skew detection and correction method, the rotation ranging from 0deg to 360deg of a Chinese handwritten word can be detected. Through the process of preprocessing, feature extraction using elastic meshing technique and classification, the handwritten words with characters even connected or partially overlapped can be recognized through a holistic approach. Experiments were performed on 8888 categories of 1,137,664 unconstrained handwritten Chinese word samples. Experimental results for randomly rotated unconstrained cursive handwritten Chinese word data demonstrated that the proposed method can achieve about 96.58% recognition accuracy.

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.

SIFT descriptor has been widely applied in computer vision and object recognition, but has not been explored in the field of handwritten Chinese character recognition. In this paper we proposed a novel SIFT based feature for offline handwritten Chinese character recognition. The presented feature is a modification of SIFT descriptor taking into account of the characteristics of handwritten Chinese samples. In our approach, global elastic meshing is first constructed and then the related gradient code of each sub-region is accumulated dynamically. Experiments using MQDF classifier show our featurepsilas effectiveness with a recognition rate of 97.868%, which outperforms original SIFT feature and two traditional features, Gabor feature and gradient feature.