摘要
中文联机手写文本切分识别要求预合并快速高效。为此,设计一个基于最小风险的多层次线性分类器。该分类器根据笔画及切分块的几何特征,采用分层合并的方式完成预合并过程。通过对联机样本进行的实验证明,该分类器保持欠切分错误率在一个较低水平的同时,有效地控制了过切分的错误率。
To solve the problem that high speed and effectiveness is needed for the pre-merger step of online Chinese handwritten text recognition,a multi-layer linear classifier based on minimum risk is provided and realized,which utilizes the geometrical features of strokes and segmented blocks and introduces the scheme of multi-layer combination.Experiments running on real samples show that the classifier reduces the over-segmentation error rate significantly while keeping the under-segmentation error rate in a very low level.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第19期138-140,共3页
Computer Engineering
基金
国家"973"计划基金资助项目"非结构化信息(图像)的内容理解与语义表征"(2001AA114081)
关键词
字符切分
预合并
最小风险
鉴别学习
多层次分类器
character segmentation
pre-merger
minimums risk
discriminative learning
multi-layer classifier
