Naxi Dongba hieroglyphs of China are the only living hieroglyphs world widely which still in use.There are thousands of manuscripts written in Dongba hieroglyphs scattering in different counties for history reason.For...Naxi Dongba hieroglyphs of China are the only living hieroglyphs world widely which still in use.There are thousands of manuscripts written in Dongba hieroglyphs scattering in different counties for history reason.For culture protection and inheritance,those manuscripts are in urgent need to be recognized and organized quickly.This paper focuses on the recognition of Naxi Dongba hieroglyphs by using coarse grid method to extract features and using support vector machine to classify.The designed Experiment shows that the method performs better than the commonly used clustering method in recognition accuracy in recognition of Naxi Dongba hieroglyphs.This method also provides some experience for recognition of other hieroglyphs.展开更多
An improved approach based on support vector machine （SVM） called the center distance ratio method is presented for license plate character recognition. First the support vectors are pre-extraeted. A minimal set cal...An improved approach based on support vector machine （SVM） called the center distance ratio method is presented for license plate character recognition. First the support vectors are pre-extraeted. A minimal set called the margin vector set, which contains all support vectors, is extracted. These margin vectors compose new training data and construct the classifier by using the general SVM optimized. The experimental resuhs show that the improved SVM method does well at correct rate and training speed.展开更多
In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English l...In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system’s performance.The experiment included news articles from various genres,such as Bollywood,politics,and sports.The proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.展开更多
基金supported by Major Programs of National Social Science Funds of China(12&ZD234)supported by Education Committee of Beijing(71E1610959)
文摘Naxi Dongba hieroglyphs of China are the only living hieroglyphs world widely which still in use.There are thousands of manuscripts written in Dongba hieroglyphs scattering in different counties for history reason.For culture protection and inheritance,those manuscripts are in urgent need to be recognized and organized quickly.This paper focuses on the recognition of Naxi Dongba hieroglyphs by using coarse grid method to extract features and using support vector machine to classify.The designed Experiment shows that the method performs better than the commonly used clustering method in recognition accuracy in recognition of Naxi Dongba hieroglyphs.This method also provides some experience for recognition of other hieroglyphs.
文摘An improved approach based on support vector machine （SVM） called the center distance ratio method is presented for license plate character recognition. First the support vectors are pre-extraeted. A minimal set called the margin vector set, which contains all support vectors, is extracted. These margin vectors compose new training data and construct the classifier by using the general SVM optimized. The experimental resuhs show that the improved SVM method does well at correct rate and training speed.
文摘In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system’s performance.The experiment included news articles from various genres,such as Bollywood,politics,and sports.The proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.
