浏览全部资源
扫码关注微信
收稿：2019-05-24，
修回：2019-8-14，
录用：2019-8-21，
纸质出版：2020-02-16
移动端阅览
目的
2
文档图像检索过程中，传统的光学字符识别（OCR）技术因易受文档图像质量和字体等相关因素的影响，难以达到有效检索的目的。关键词识别技术作为OCR技术的替代方案，不需经过繁琐的OCR识别，可直接对关键词进行检索。本文针对Harris算法聚簇现象严重和运算速度慢等问题，在关键词识别技术的框架下提出了改进Harris的图像匹配算法。
方法
2
基于Fast进行特征点检测，利用Harris进行特征描述，并采用非极大值抑制的方法，最后利用暴力匹配中的汉明距离进行特征的相似性度量，输出最终的匹配结果。
结果
2
实验结果表明本文算法在特征提取上的时间为0.101 s，相对于原始Harris算法的0.664 s和SIFT算法的1.066 s，实时性方面有了明显提高，改善了原始算法的聚簇现象，并且在无噪声的情况下，准确率达到98%，高于Sift算法的90%，召回率达到87.5%，而且在固定均值，不断提高方差的高斯噪声条件下，与Sift算法相比，准确率也高于后者，取得了较好的实验效果。
结论
2
本文提出的方法满足了快速、精确的查找需求，在印刷体图像的文档图像检索中有效提高了检索率，具有较好的实验效果。
Objective
2
In the 21st century
the rapid development of Internet information provides great convenience for people's lifestyles
but people also must face information redundancy when they go online because most information is now in text. The existence of forms emphasizes the importance of accurately and efficiently obtaining the information that users need. Moreover
with the acceleration of informationization
the number of electronic documents has risen sharply
making the efficient and fast retrieval of document images further urgent. In document image retrieval
traditional optical character recognition (OCR) technology has difficulty in achieving effective retrieval due to the quality of document images and fonts. As an alternative to OCR technology
word recognition technology does not require cumbersome OCR recognition and can directly search for keywords to achieve good results. In keyword extraction
local feature extraction has a more detailed and accurate description than global feature extraction. In terms of corner detection
this paper focuses on the serious clustering and slow computing speed of Harris algorithm.
Method
2
In the framework of word-spotting technology
an improved Harris image matching algorithm is proposed
which is used for document image retrieval for the first time. First
the original Harris algorithm uses the Gaussian function to smooth the window in the feature point extraction process of the image. When calculating the corner response value R
the differential operator is used as the directional derivative to calculate the number of multiplication operations
resulting in many computational algorithms
slow operation
and other issues. Given the deficiencies of the original Harris algorithm
FAST algorithm is used in the detection of corner points. 1) The gray values of the center pixel and surrounding 16 pixels are compared using the formula in the radius 3 field. 2) To improve the detection speed
the 0th and 8th pixel points on the circumference are first detected
and the two points on the other diameters are sequentially detected. 3) A difference between the gray values of 12 consecutive points and the p-point exceeding the threshold indicates a corner point. 4) After obtaining the primary corner
the Harris algorithm is used to remove the pseudo corner. Second
the original Harris algorithm sorts and compares the local maximum of the corner response function
establishes the response and coordinate matrices
records the local maximum and response coordinates
and compares the global maximum. At this point
all corner points have been recorded
but a case wherein multiple corner points coexist in the domain of a corner point
namely
"clustering" phenomenon
is likely. To address the serious clustering problem of the Harris algorithm
a nonmaximum value suppression method is adopted
which essentially searches for the local maximum and suppresses nonmaximum elements. When detecting the diagonal points
the local maximum is sorted from large to small
the suppression radius is set
a new response function matrix is established
and the corner points are extracted by continuously reducing the radius
thereby effectively avoiding Harris corner clustering. 1) The value of the corner response function of all pixels in the graph is calculated
the local maximum is searched for
and the pixel of the local maximum is recorded. 2) The local maximum ordering matrix and corresponding coordinate matrix are established
and the local maxima are sorted from large to small. 3) The suppression radius is set to the local maximum
a new matrix of response functions is established
and the corner points are extracted by continuously reducing the radius. 4) Whether the local maximum value is the maximum value within the suppression radius
that is
the desired corner point
is judged; if the condition is satisfied
then the local maximum value is added to the response function matrix. 5) The radius reduction is continued to extract the corner points. If the number of corner points is preset
then the process ends. Otherwise
step 4) is repeated.
Result
2
Experimental results show that the accuracy rate is 98% and the recall rate is 87.5% without noise. Good experimental results are obtained under the condition of constant mean and continuous improvement of variance of Gaussian noise. Compared with the SIFT algorithm
the time is considerably improved
and the accuracy is increased.
Conclusion
2
Starting from the document image of the printed matter
FAST+Harris is used to search for keywords under the framework of keyword recognition technology. On the one hand
this method saves retrieval time and improves the real-time performance of the algorithm. On the other hand
it improves the aggregation of Harris. The cluster phenomenon improves the accuracy of corner detection. Compared with the SIFT algorithm
time is greatly improved
and good experimental results are achieved under the influence of different degrees of noise.
Aldana-Murillo N G, Hayet J B and Becerra H M. 2015. Evaluation of local descriptors for vision-based localization of humanoid robots//Proceedings of the 7th Mexican Conference on Pattern Recognition. Mexico City, Mexico: Springer International Publishing: 179-189[ DOI: 10.1007/978-3-319-19264-2_18 http://dx.doi.org/10.1007/978-3-319-19264-2_18 ]
Aliya B. 2017. Research on Uyghur Printed Complex Document Image Retrieval Based on Local Feature. Urumqi: Xinjiang University
阿丽亚·巴吐尔. 2017.基于局部特征的维吾尔文印刷体复杂文档图像检索研究.乌鲁木齐: 新疆大学
Aliya B, Nurbiya Y, Hornisa M, Alimjan A and Kurban U. 2019. Complex Uyghur document image matching and retrieval based on modified SURF feature. CAAI Transactions on Intelligent Systems, 14(2):296-305
阿丽亚·巴吐尔, 努尔毕亚·亚地卡尔, 吾尔尼沙·买买提, 阿力木江·艾沙, 库尔班·吾布力. 2019.改进SURF特征的维吾尔文复杂文档图像匹配检索.智能系统学报, 14(2):296-305)[DOI:10.11992/tis.201709014]
Almazán J, Gordo A, Fornés A and Valveny. 2014. Word spotting and recognition with embedded attributes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(12):2552-2566[DOI:10.1109/tpami.2014.2339814]
Chen S Z and Wang W Y. 2010. An improved corner detection algorithm based on Harris. Modern Computer, (6):44-46, 57
陈书智, 王未央. 2010.基于Harris角点检测的改进算法.现代计算机(专业版), (6):44-46, 57)[DOI:10.3969/j.issn.1007-1423-B.2010.06.012]
Giotis A P, Sfikas G, Gatos B and Nikou C. 2017. A survey of document image word spotting techniques. Pattern Recognition, 68:310-332[DOI:10.1016/j.patcog.2017.02.023]
Hu H W. 2018. Research on Deep Learning for Historical Mongolian Document Images Retrieval. Hohhot: Inner Mongolia University
胡宏伟. 2018.基于深度学习的蒙古文古籍图像检索技术研究.呼和浩特: 内蒙古大学
Hussain R, Khan H A, Siddiqi I, Khurshid K and Masood A. 2016. Keyword based information retrieval system for Urdu document images//Proceedings of the 11th International Conference on Signal-image Technology and Internet-based Systems. Bangkok, Thailand: IEEE: 27-33[ DOI: 10.1109/SITIS.2015.16 http://dx.doi.org/10.1109/SITIS.2015.16 ]
Khayyat M, Lam L and Suen C Y. 2014. Learning-based word spotting system for Arabic handwritten documents. Pattern Recognition, 47(3):1021-1030[DOI:10.1016/j.patcog.2013.08.014]
Lee D R, Hong W and Oh I S. 2012. Segmentation-free word spotting using SIFT//Proceedings of 2012 IEEE Southwest Symposium on Image Analysis and Interpretation. Santa Fe, NM, USA: IEEE: 65-68[ DOI: 10.1109/SSIAI.2012.6202454 http://dx.doi.org/10.1109/SSIAI.2012.6202454 ]
Mhiri M, Desrosiers C and Cheriet M. 2019. Word spotting and recognition via a joint deep embedding of image and text. Pattern Recognition, 88:312-320
Obaidullah S M, Santosh K C, Das N, Halder C and Roy K. 2018. Handwritten Indic script identification in multi-script document images:a survey. International Journal of Pattern Recognition and Artificial Intelligence, 32(10):1856012[DOI:10.1142/S0218001418560128]
Sfikas G, Giotis A P, Louloudis G and Gatos B. 2015. Using attributes for word spotting and recognition in Polytonic Greek documents//Proceedings of the 13th International Conference on Document Analysis and Recognition. Tunis, Tunisia:IEEE, 1:686-690[DOI:10.1109/ICDAR.2015.7333849]
Singhai N and Shandilya S K. 2010. A survey on:content based image retrieval systems. International Journal of Computer Applications, 4(2):22-26[DOI:10.5120/802-1139]
Stauffer M, Fischer A and Riesen K. 2018. Filters for graph-based keyword spotting in historical handwritten documents. Pattern Recognition Letters: 1-10[ DOI: 10.1016/j.patrec.2018.03.030 http://dx.doi.org/10.1016/j.patrec.2018.03.030 ]
Tan C L, Zhang X and Li L L. 2014. Image based retrieval and keyword spotting in documents//Doermann D and Tombre K. Handbook of Document Image Processing and Recognition. London: Springer: 805-842[ DOI: 10.1007/978-0-85729-859-1_27 http://dx.doi.org/10.1007/978-0-85729-859-1_27 ]
Wang R and Zhang B. 2015. The Harris corners detection method based on self-adapting non-maximal supperssion algorithm. Value Engineering, 34(8):267-269
王瑞, 张波. 2015.基于自适应非最大抑制的Harris角点检测算法.价值工程, 34(8):267-269)[DOI:10.14018/j.cnki.cn13-1085/n.2015.08.151]
Wei H X, Gao G L and Su X D. 2015. A multiple instances approach to improving keyword spotting on historical Mongolian document images//Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR). Tunis, Tunisia: IEEE: 121-125[ DOI: 10.1109/ICDAR.2015.7333738 http://dx.doi.org/10.1109/ICDAR.2015.7333738 ]
Wen Z X, Bao F L, Gao G L, Wang Y H and Su X D. 2018. Design and implementation of Mongolian information retrieval system. Journal of Chinese Information Processing, 32(7):44-51, 57
温子潇, 包飞龙, 高光来, 王勇和, 苏向东. 2018.蒙古文信息检索系统的设计与实现.中文信息学报, 32(7):44-51, 57)[DOI:10.3969/j.issn.1003-0077.2018.07.006]
Wshah S, Kumar G and Govindaraju V. 2012. Multilingual word spotting in offline handwritten documents//Proceedings of the 21st International Conference on Pattern Recognition. Tsukuba, Japan: IEEE: 310-313
Yalniz I Z and Manmatha R. 2019. Dependence models for searching text in document images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1):49-63[DOI:10.1109/TPAMI.2017.2780108]
Yan X P, Hao L and Yang X. 2017. Real-time Harris corner detection method based on FPGA. Application Research of Computers, 34(12):3848-3851
闫小盼, 敖磊, 杨新. 2017. Harris角点检测的FPGA快速实现方法.计算机应用研究, 34(12):3848-3851)[DOI:10.3969/j.issn.1001-3695.2017.12.074]
相关作者
相关机构
京公网安备11010802024621
