HOG特征值的笔迹鉴定算法  

Handwriting identification based on HOG feature values

作　　者：杨东 王以松[1] YANG Dong;WANG Yisong(College of Computer Science and Technology,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学计算机科学与技术学院,贵阳550025

出　　处：《智能计算机与应用》2023年第2期161-164,共4页Intelligent Computer and Applications

摘　　要：机器学习作为人工智能的一个分支,在工程实践中已经产生了较大的经济价值和科技价值。机器学习创建基于样本的数学模型,通过训练预测或者作出决策解决人工智能中的问题。支持向量机是按监督学习方式对数据进行二元分类的广义线性分类器,其决策边界是对学习样本求解的最大边距超平面。本文通过提取笔迹中的HOG特征,再利用支持向量机对该特征值进行训练,得到笔迹鉴定的模型,并通过该模型鉴定笔迹。Machine learning,a branch of artificial intelligence,has generated a great economic and scientific value in engineering practice.Machine learning solves problems in artificial intelligence by creating sample-based mathematical models that are trained to predict or make decisions.Support Vector Machine(SVM)is a class of generalized linear classifier that performs binary classification of data in a supervised learning manner,where the decision boundary is a maximum margin hyperplane solved for the learned samples.In this paper,we extract the HOG features in handwriting and then use the feature values for training using SVM,thus obtaining a model for handwriting identification.The experimental results verify its good effect.

关 键 词：机器学习 支持向量机 笔迹鉴定 HOG特征 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]