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It is difficult to classify object or scene images with high accuracy when the dataset is relatively large. Spatial Pyramid Matching (SPM) was proposed to deal with this problem, but there are some shortages. As an improvement for SPM, we proposed three pieces of meliorations: first, use approximate nearest neighbor method instead of k-means for clustering; second, regulate the size of codebook referring...
Lacking of dataset is still a serious problem for researchers who study on online handwriting word recognition (HWR). In this paper, a handwritten Chinese word synthesis method is proposed for the first time to generate a large scale handwritten Chinese word dataset. The distributions of shape and position characteristics, such as aspect radio, character interval and the angle of gravity center line...
Writer adaptation has been proved to be an effective approach to improve the recognition performance of the writer-independent recognizer for a particular writer. In this paper, we propose a writer adaptive handwriting recognition approach by incremental learning the Modified Quadratic Discriminant Function (MQDF) classifier. We derived the solution of Incremental MQDF (IMQDF) and then present a Discriminative...
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...
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...
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