公开(公告)号：US20100239168A1

公开(公告)日：2010-09-23

申请号：US12407791

申请日：2009-03-20

Applicant: Qiang Huo ,  Yongqiang Wang

Inventor： Qiang Huo ,  Yongqiang Wang

IPC: G06K9/18

CPC classification number: G06K9/6278

Abstract: Described is a technology by which handwriting recognition is performed using a semi-tied covariance modeling (STC) that requires far less memory than other models such as MQDF. Offline training, such as via maximum likelihood and/or minimum classification error techniques, provides classification data. The classification data includes semi-tied transforms that are shared by classes, along with a class-dependent diagonal matrix and a mean vector corresponding to each class. The semi-tied transforms and class-dependent diagonal matrices are obtained by processing a precision matrix for each class. In online recognition, received handwritten input (e.g., an East Asian character) is classified into a class, based upon the class-dependent diagonal matrices and the semi-tied transforms, by a STC recognizer that outputs similarity scores for candidates and a decision rule that selects the most likely class.

Abstract translation: 描述了使用半绑合协方差建模（STC）执行手写识别的技术，其需要比其他模型如MQDF少得多的存储器。 离线训练，例如通过最大似然和/或最小分类错误技术，提供分类数据。 分类数据包括由类共享的半连接变换，以及类依赖对角矩阵和对应于每个类的平均向量。 通过处理每个类的精度矩阵获得半绑定变换和类依赖对角矩阵。 在在线识别中，通过输出候选人的相似性分数的STC识别器和决策规则，基于类依赖对角矩阵和半连接变换将接收到的手写输入（例如，东亚字符）分类为类 选择最可能的类。