It still is a difficult task for handwritten chinese character recognition (HCCR) to put into practical use. An efficient classifier occupies very important position for increasing offline HCCR rate. SVMs offer a theoretically well-founded approach to automated learning of pattern classifiers for mining labeled data sets. As we know, the performance of SVM largely depends on the kernel function. In this paper, we investigated the classification performance of SVMs
with various common kernels in HCCR. We found that except for sigmoid kernel, SVMs with polynomial kernel, linear kernel, RBF kernel and multi-quadratic kernel are all efficient classifier for HCCR, their behavior has a little difference, taking one with another, SVM with multi-quadratic kernel is the best.
Offline handwritten Chinese character recognition is one of the difficult problems in pattern recognition area because of its large stroke distortion, writing anomaly, and no stroke ranking information can be gotten. The basic characteristic of Chinese character is that it is composed of four kinds of stroke, i.e. horizontal, vertical, 45 degree direction and 135 degree direction. A Chinese character can be uniquely confirmed by the quantity of the four directional strokes and its relative position. From the contour of Chinese character, we can get the features mentioned above. In this paper, we proposed first to modify an existed contour extraction algorithm and obtained strict single pixel contour of Chinese character, and then to give a contour-based elastic mesh fuzzy feature extraction method. Comparison experimental results show that the performance of our approaches is encouraging and can be comparable to other algorithms.
With the recent explosion of interest in microarray technology, large amounts of microarray images are being produced currently. Since there is no standard method for information extracting, the storage and the transmission of this type of data are becoming increasingly challenging. Here we present a new segmentation template extracted method and propose a new lossless compression scheme. Our segmentation scheme is based on mean shift filtering and morphological H-reconstruction that can accurately segment microarray images. Based on the extracted segmentation template, our compression scheme divides image into foreground regions and background region and code each region separately. Particularly, two 16-bit images sharing one segmentation template and the segmentation template are compressed into one file. Experimental results and comparison with Gzip that commonly used in microarray management showed that our scheme is efficient and also can greatly facilitate the downstream information extraction and analysis.
Offline handwritten chinese character recognition (HCCR) is still a difficult problem because of its large stroke changes, writing anomaly, and the difficulty for obtaining its stroke ranking information. Generally, offline HCCR can be divided into two procedures: feature extraction for capturing handwritten Chinese character information and feature classifying for character recognition. In this paper, we proposed a new chinese character recognition algorithm. In feature extraction part, we adopted elastic mesh dividing method for extracting the block features and its relative fuzzy features that utilized the relativities between different strokes and distribution probability of a stroke in its neighbor sub-blocks. In recognition part, we constructed a classifier based on a supervisory competitive learning algorithm to train competitive learning neural network with the extracted features set. Experimental results show that the performance of our algorithm is encouraging and can be comparable to other algorithms.
Fiber optic is the best medium for communication, FDDI, Fiber Distributed Data Interface, is the popular computer LAN over fiber optic. In this paper, we describe in details the design and implementation of our FDDI microcomputer network system named Sunrays, the stress will be lain on how the communication is realized in this network system by optic-related components.
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