基于Gist和IPCA算法的多文种离线手写签名识别  

Multilingual offline handwritten signature recognition based on Gist and IPCA

作　　者：韩辉 麦合甫热提 吾尔尼沙·买买提 朱亚俐[1] 库尔班·吾布力[1,2] HAN Hui;Mahpirat;Hornisa Mamat;ZHU Ya-li;Kurban Ubul(School of Information Science and Engineering,Xinjiang University,Urumqi 830046;Key Laboratory of Xinjiang Multilingual Information Technology,Urumqi 830046;Office of Educational Administration,Xinjiang University,Urumqi 830046,China)

机构地区：[1]新疆大学信息科学与工程学院,新疆乌鲁木齐830046 [2]新疆多语种信息技术重点实验室,新疆乌鲁木齐830046 [3]新疆大学教务处,新疆乌鲁木齐830046

出　　处：《计算机工程与科学》2022年第11期2048-2055,共8页Computer Engineering & Science

基　　金：国家自然科学基金(61862061,61563052,62061045);新疆大学博士启动基金(BS180268);新疆维吾尔自治区研究生科研创新项目(XJ2019G064,XJ2020G064)。

摘　　要：由于离线手写签名图像有效的笔画部分普遍比较稀疏,存在大量的无效白色背景,目前常用的特征描述方法会使得得到的特征数据存在大量冗余,影响识别准确率。识别准确率的提高,需要依赖大量的训练数据和提取多个特征并进行融合,但这样又会因特征数据过多和维度过大而造成计算困难,影响识别效率。为此,提出了一种基于Gist和IPCA算法的多文种离线手写签名识别方法,利用Gist特征聚焦图像的整体布局和笔画部分,同时利用IPCA算法的批处理能力来提高识别效果和运行效率。使用中、英、维3种语言的实验数据集,并使用SVM分类器进行识别实验。结果显示,3个数据集上的识别准确率分别为97.97%,98.43%和97.19%,3种数据混合后的识别准确率为97.70%。经过对比分析可知,提出的方法与之前的相关方法相比明显较优。Because the effective strokes of offline handwritten signature images are generally sparse,and there are lots of invalid white backgrounds,using the commonly used feature description methods will cause a lot of re-dundancy in the obtained feature data,which will affect the recognition accuracy.In order to improve the recognition accuracy,we either need to rely on a large number of training data or extract multiple features for fusion,which will cause difficulty in the calculation and affect the efficiency of the experiment due to too much feature data and too large dimensions.Therefore,this paper proposes a multilingual off-line hand-written signature recognition method based on the Gist and IPCA algorithms,which uses gist features to focus on the overall layout and strokes of the image,and the batch processing ability of the IPCA algorithm to improve the recognition effect and operation efficiency.Three experimental datasets(Chinese,English,and Uyghur)and the SVM classifier are used in the recognition experiments.The results show that the recognition accuracy of the three data sets is 97.97%,98.43%,and 97.19%respectively,and the recognition accuracy of the three mixed data sets is 97.7%.Comparative analysis shows that the proposal is obviously better than the previous related research.

关 键 词：多文种 手写签名识别 Gist特征 IPCA算法 SVM 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]