One of the most important methods that finds usefulness in various applications, such as searching historical manuscripts, forensic search, bank check reading, mail sorting, book and handwritten notes transcription, i...One of the most important methods that finds usefulness in various applications, such as searching historical manuscripts, forensic search, bank check reading, mail sorting, book and handwritten notes transcription, is handwritten character recognition. The common issues in the character recognition are often due to different writing styles, orientation angle, size variation(regarding length and height), etc. This study presents a classification model using a hybrid classifier for the character recognition by combining holoentropy enabled decision tree(HDT) and deep neural network(DNN). In feature extraction, the local gradient features that include histogram oriented gabor feature and grid level feature, and grey level co-occurrence matrix(GLCM) features are extracted. Then, the extracted features are concatenated to encode shape, color, texture, local and statistical information, for the recognition of characters in the image by applying the extracted features to the hybrid classifier. In the experimental analysis, recognition accuracy of 96% is achieved. Thus, it can be suggested that the proposed model intends to provide more accurate character recognition rate compared to that of character recognition techniques used in the literature.展开更多
针对PHM(Prognostic and Health Management)中数据挖掘和知识获取困难的问题,提出一种以J48决策树算法为基础的故障诊断方法。采用了开源数据挖掘软件Weka,对CTSV滤波器故障仿真数据进行计算,对故障数据进行属性清理和参数选择。生成...针对PHM(Prognostic and Health Management)中数据挖掘和知识获取困难的问题,提出一种以J48决策树算法为基础的故障诊断方法。采用了开源数据挖掘软件Weka,对CTSV滤波器故障仿真数据进行计算,对故障数据进行属性清理和参数选择。生成的决策树模型有很高的交叉验证率和分类效果。展开更多
文摘One of the most important methods that finds usefulness in various applications, such as searching historical manuscripts, forensic search, bank check reading, mail sorting, book and handwritten notes transcription, is handwritten character recognition. The common issues in the character recognition are often due to different writing styles, orientation angle, size variation(regarding length and height), etc. This study presents a classification model using a hybrid classifier for the character recognition by combining holoentropy enabled decision tree(HDT) and deep neural network(DNN). In feature extraction, the local gradient features that include histogram oriented gabor feature and grid level feature, and grey level co-occurrence matrix(GLCM) features are extracted. Then, the extracted features are concatenated to encode shape, color, texture, local and statistical information, for the recognition of characters in the image by applying the extracted features to the hybrid classifier. In the experimental analysis, recognition accuracy of 96% is achieved. Thus, it can be suggested that the proposed model intends to provide more accurate character recognition rate compared to that of character recognition techniques used in the literature.
文摘针对PHM(Prognostic and Health Management)中数据挖掘和知识获取困难的问题,提出一种以J48决策树算法为基础的故障诊断方法。采用了开源数据挖掘软件Weka,对CTSV滤波器故障仿真数据进行计算,对故障数据进行属性清理和参数选择。生成的决策树模型有很高的交叉验证率和分类效果。
