字符串 ') and Issue_No=(select Issue_No from OA where Script_ID=@Script_ID) order by ID ' 后的引号不完整。 ') and Issue_No=(select Issue_No from OA where Script_ID=@Script_ID) order by ID ' 附近有语法错误。 基于图表示和匹配的表单定位与提取-《智能系统学报》

[1]谭婷,吕淑静,吕岳.基于图表示和匹配的表单定位与提取[J].智能系统学报,2019,14(02):231-238.[doi:10.11992/tis.201709038]
 TAN Ting,LYU Shujing,LYU Yue.Form location and extraction based on graph representation and matching[J].CAAI Transactions on Intelligent Systems,2019,14(02):231-238.[doi:10.11992/tis.201709038]
点击复制

基于图表示和匹配的表单定位与提取(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年02期
页码:
231-238
栏目:
出版日期:
2019-03-05

文章信息/Info

Title:
Form location and extraction based on graph representation and matching
作者:
谭婷1 吕淑静2 吕岳12
1. 华东师范大学 上海多维度信息处理重点实验室, 上海 200062;
2. 中国邮政集团公司上海研究院 图像分析与智能系统联合实验室, 上海 200062
Author(s):
TAN Ting1 LYU Shujing2 LYU Yue12
1. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200062, China;
2. ECNU-SRI Joint Lab for Pattern Analysis and Intelligent System, Shanghai Research Institute of China Post, Shanghai 200062, China
关键词:
图像分割表单提取表单定位图表示图匹配同构图快递包裹分拣
Keywords:
image segmentationform extractionform locationgraph representationgraph matchingisomorphic graphexpress package sorting
分类号:
TP751.1
DOI:
10.11992/tis.201709038
摘要:
为了实现对不同类型、分辨率和方向的快递表单上用户感兴趣区域信息的获取,本文提出了一种基于图表示和匹配的表单定位与提取方法。选择参考表单中已有的印刷图案或字符等关键区域作为基准位置,进行图的表示。基于图像分割得到的候选关键区域对待处理表单进行图表示。然后,根据图的属性计算待处理表单与参考表单的相似度。最后,将最大相似度对应的同构图作为参考表单图的最优匹配,并建立同构图与参考表单图位置映射,定位出表单。本文实验数据集来源于真实场景下采集的快递包裹表单图像。实验结果表明:本文算法在快递包裹表单图像上具有良好的性能,对旋转、光照变化、局部遮挡具有较好的鲁棒性。
Abstract:
To obtain information of a user’s interested region on express package images of different types, resolutions, and directions, a form location and extraction method based on graph representation and matching is proposed in this paper. A reference form is needed in this method. First, key regions such as the existing printed patterns or characters in the reference form are chosen as nodes to build the reference graph. Second, graph representation is conducted on the form to be processed based on the candidate key region derived from image segmentation. Then, the similarity between the reference form and the candidate form is calculated according to attributes of the graph. Finally, the isomorphic graph with the maximum similarity is chosen as the optimal matching of the reference form and graph, and the position mapping of the isomorphic graph and the reference form and test image is established to locate the form. The experimental datasets in this paper originate from express package images collected in practical scenarios. Experimental results indicate that the proposed algorithm has good performance on express form images. Especially, good robustness is achieved for rotated, illuminated, and partially shaded images.

参考文献/References:

[1] SHARMA D V, LEHAL G S. Form field frame boundary removal for form processing system in Gurmukhi script[C]//Proceedings of the 10th International Conference on Document Analysis and Recognition. Barcelona, Spain, 2009:256-260.
[2] CHEN J L, LEE H J. An efficient algorithm for form structure extraction using strip projection[J]. Pattern recognition, 1998, 31(9):1353-1368.
[3] LIU Wenyin, DORI D. From raster to vectors:extracting visual information from line drawings[J]. Pattern analysis and applications, 1999, 2(1):10-21.
[4] WATANABE T, LUO Qin, SUGIE N, et al. Layout recognition of multi-kinds of table-form documents[J]. IEEE transactions on pattern analysis and machine intelligence, 1995, 17(4):432-445.
[5] LAM S W, SRIHARI S N. Multi-domain document layout understanding[C]//Proceedings of International Conference on Document Analysis and Recognition. 1991:112-120.
[6] SACHDEVA R, SHARMA D V. Data extraction from hand-filled form using form template[J]. International journal on recent and innovation trends in computing and communication, 2015, 3(8):5311-5317.
[7] NING L W, SIAH Y K, KHALID M, et al. Design of an automated data entry system for hand-filled forms[C]//Proceedings of 2000 TENCON. Kuala Lumpur, Malaysia, 2000:162-166.
[8] BENSEFIA A. Extraction of Arabic handwriting fields by forms matching[J]. Journal of signal and information processing, 2015, 6(1):53424.
[9] CESARINI F, GORI M, MARINAI S, et al. INFORMys:a flexible invoice-like form-reader system[J]. IEEE transactions on pattern analysis and machine intelligence, 1998, 20(7):730-745.
[10] CHO M, SUN Jian, DUCHENNE O, et al. Finding matches in a haystack:a max-pooling strategy for graph matching in the presence of outliers[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:2091-2098.
[11] SUH Yumin, ADAMCZEWSKI K, LEE K M. Subgraph matching using compactness prior for robust feature correspondence[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA, 2015:5070-5078.
[12] SHARMA A, HORAUD R, CECH J, et al. Topologically-robust 3D shape matching based on diffusion geometry and seed growing[C]//Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA, 2011:2481-2488.
[13] DUCHENNE O, JOULIN A, PONCE J. A graph-matching kernel for object categorization[C]//Proceedings of 2011 IEEE International Conference on Computer Vision. Barcelona, Spain, 2011:1792-1799.
[14] ZHANG Quanshi, SONG Xuan, SHAO Xiaowei, et al. Attributed graph mining and matching:an attempt to define and extract soft attributed patterns[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:1394-1401.
[15] ZHANG Quanshi, SONG Xuan, SHAO Xiaowei, et al. Object discovery:soft attributed graph mining[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 38(3):532-545.
[16] LEORDEANU M, SUKTHANKAR R, Hebert M, et al. Unsupervised learning for graph matching[J]. International journal of computer vision, 2012, 96(1):28-45.
[17] UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al. Selective search for object recognition[J]. International journal of computer vision, 2013, 104(2):154-171.

相似文献/References:

[1]王科俊,郭庆昌.基于粒子群优化算法和改进的Snake模型的图像分割算法[J].智能系统学报,2007,2(01):53.
 WANG Ke-jun,GUO Qing-chang.Image segmentation algorithm based on the PSO and improved Snake model[J].CAAI Transactions on Intelligent Systems,2007,2(02):53.
[2]陈小波,程显毅.一种基于MAS的自适应图像分割方法[J].智能系统学报,2007,2(04):80.
 CHEN Xiao-bo,CHENG Xian-yi.An adaptive image segmentation technique based on multiAgent system[J].CAAI Transactions on Intelligent Systems,2007,2(02):80.
[3]刘咏梅,代丽洁.基于空间位置约束的K均值图像分割[J].智能系统学报,2010,5(01):67.
 LIU Yong-mei,DAI Li-jie.An improved method of Kmeans image segmentation based on spatial position information[J].CAAI Transactions on Intelligent Systems,2010,5(02):67.
[4]吴一全,纪守新.灰度熵和混沌粒子群的图像多阈值选取[J].智能系统学报,2010,5(06):522.
 WU Yi-quan,JI Shou-xin.Multithreshold selection for an image based on gray entropy and chaotic particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2010,5(02):522.
[5]尚倩,阮秋琦,李小利.双目立体视觉的目标识别与定位[J].智能系统学报,2011,6(04):303.
 SHANG Qian,RUAN Qiuqi,LI Xiaoli.Target recognition and location based on binocular stereo vision[J].CAAI Transactions on Intelligent Systems,2011,6(02):303.
[6]胡光龙,秦世引.动态成像条件下基于SURF和Mean shift的运动目标高精度检测[J].智能系统学报,2012,7(01):61.
 HU Guanglong,QIN Shiyin.High precision detection of a mobile object under dynamic imaging based on SURF and Mean shift[J].CAAI Transactions on Intelligent Systems,2012,7(02):61.
[7]马慧,王科俊.采用旋转校正的指静脉图像感兴趣区域提取方法[J].智能系统学报,2012,7(03):230.
 MA Hui,WANG Kejun.A region of interest extraction method using rotation rectified finger vein images[J].CAAI Transactions on Intelligent Systems,2012,7(02):230.
[8]尹雨山,王李进,尹义龙,等.回溯搜索优化算法辅助的多阈值图像分割[J].智能系统学报,2015,10(01):68.[doi:10.3969/j.issn.1673-4785.201410008]
 YIN Yushan,WANG Lijin,YIN Yilong,et al.Backtracking search optimization algorithm assisted multilevel threshold for image segmentation[J].CAAI Transactions on Intelligent Systems,2015,10(02):68.[doi:10.3969/j.issn.1673-4785.201410008]
[9]吴一全,王凯,曹鹏祥.蜂群优化的二维非对称Tsallis交叉熵图像阈值选取[J].智能系统学报,2015,10(01):103.[doi:10.3969/j.issn.1673-4785.201403040]
 WU Yiquan,WANG Kai,CAO Pengxiang.Two-dimensional asymmetric tsallis cross entropy image threshold selection using bee colony optimization[J].CAAI Transactions on Intelligent Systems,2015,10(02):103.[doi:10.3969/j.issn.1673-4785.201403040]
[10]龙鹏,鲁华祥.方差不对称先验信息引导的全局阈值分割方法[J].智能系统学报,2015,10(5):663.[doi:10.11992/tis.201412022]
 LONG Peng,LU Huaxiang.Global threshold segmentation technique guided by prior knowledge with asymmetric variance[J].CAAI Transactions on Intelligent Systems,2015,10(02):663.[doi:10.11992/tis.201412022]

备注/Memo

备注/Memo:
收稿日期:2017-09-20。
作者简介:谭婷,女,1992年生,硕士研究生,主要研究方向为图像处理与计算机视觉。;吕淑静,女,1977年生,高级工程师,博士,主要研究方向为图像处理与计算机视觉。;吕岳,男,1968年生,教授,博士生导师,主要研究方向为模式识别、图像处理、生物特征识别、信息检索、数据挖掘、自然语言处理、机器视觉系统。教育部新世纪优秀人才计划、上海市曙光学者、上海市优秀技术带头人、上海市领军人才、交通运输部优秀科技人员和优秀科技创新团队带头人。9次获得省部级科学技术奖,其中一等奖4次。授权发明专利13项。发表学术论文100余篇。
通讯作者:谭婷.E-mail:tanting_hn@163.com
更新日期/Last Update: 2019-04-25