Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.
In this paper we proposed an effective but simple table recognition algorithm in the OCR field. First a binary image template is built for the tables to be recognized, which consists of all the lines describing the table cells we are interested in. Then the images to be analyzed are thresholded and deskewed. Vertical and horizontal lines are extracted from the preprocessed image to form a table “scene”. Finally the binary image template is aligned to the table “scene” by minimizing their Hausdorff distance. From the alignment the image regions of interest corresponding to the table cells are extracted for further recognition task. Experiments show that the proposed method with the template can cope with many low quality images and achieve good recognition results.
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