中国科学院自动化研究所机构知识库
Advanced  
CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习团队  > 期刊论文
题名: Writer Adaptation with Style Transfer Mapping
作者: Zhang Xuyao(张煦尧)1; Liu Chenglin(刘成林)1
刊名: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期: 2013-07-01
卷号: 35, 期号:7, 页码:1773-1787
关键词: Writer adaptation ; style transfer mapping ; handwriting recognition
文章类型: Article
英文摘要: Adapting a writer-independent classifier toward the unique handwriting style of a particular writer has the potential to significantly increase accuracy for personalized handwriting recognition. This paper proposes a novel framework of style transfer mapping (STM) for writer adaptation. The STM is a writer-specific class-independent feature transformation which has a closed-form solution. After style transfer mapping, the data of different writers are projected onto a style-free space, where the writer-independent classifier needs no change to classify the transformed data and can achieve significantly higher accuracy. The framework of STM can be combined with different types of classifiers for supervised, unsupervised, and semi-supervised adaptation, where writer-specific data can be either labeled or unlabeled and need not cover all classes. In this paper, we combine STM with the state-of-the-art classifiers for large-category Chinese handwriting recognition: learning vector quantization (LVQ) and modified quadratic discriminant function (MQDF). Experiments on the online Chinese handwriting database CASIA-OLHWDB demonstrate that STM-based adaptation is very efficient and effective in improving classification accuracy. Semi-supervised adaptation achieves the best performance, while unsupervised adaptation is even better than supervised adaptation. On handwritten text data, semi-supervised adaptation achieves error reduction rates 31.95 and 25.00 percent by LVQ and MQDF, respectively.
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]: Computer Science ; Engineering
关键词[WOS]: HANDWRITTEN CHARACTER-RECOGNITION ; CLASSIFICATION ; CLASSIFIERS ; MODELS
收录类别: SCI
语种: 英语
WOS记录号: WOS:000319060600018
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ia.ac.cn/handle/173211/3072
Appears in Collections:模式识别国家重点实验室_模式分析与学习团队_期刊论文

Files in This Item: Download All
File Name/ File Size Content Type Version Access License
XYZ2013-style_transfer_mapping-PAMI.pdf(2689KB)期刊论文作者接受稿开放获取View Download

作者单位: 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China

Recommended Citation:
Zhang, Xu-Yao,Liu, Cheng-Lin. Writer Adaptation with Style Transfer Mapping[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2013,35(7):1773-1787.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Zhang, Xu-Yao]'s Articles
[Liu, Cheng-Lin]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Zhang, Xu-Yao]‘s Articles
[Liu, Cheng-Lin]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
文件名: XYZ2013-style_transfer_mapping-PAMI.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2017  中国科学院自动化研究所 - Feedback
Powered by CSpace