Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11): 163-169.doi: 10.11896/jsjkx.210900225

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Handwritten Image Binarization Based on Background Estimation and Local Adaptive Integration

HE Huang-xing1, CHEN Ai-guo1, WANG Jiao-long2   

  1. 1 School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi,Jiangsu 214122,China
    2 School of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China
  • Received:2021-09-27 Revised:2022-03-15 Online:2022-11-15 Published:2022-11-03
  • About author:HE Huang-xing,born in 1997,master.His main research interests include ima-ge processing and machine learning.
    CHEN Ai-guo,born in 1975,associate professor.His main research interests include machine learning,artificial intelligence and pattern recognition.
  • Supported by:
    National Natural Science Foundation of China(61702225).

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Abstract: In handwritten document images,there are often uneven lighting,ink stains,paper degradation,shadows and other complex conditions.In order to solve the problem that OCR effect of document image is not ideal after binarization in complex background,a binarization method of improved background estimation and local adaptive integration is proposed.In this method,the local adaptive binarization method is first used to obtain the binarization image with high recall rate,then the improved background estimation method is used to obtain the binarization image with high accuracy rate,and finally the two types of binarization images are integrated based on the connected domain method to obtain the final binarization image.Experimental results on DIBCO2013 and DIBCO2016 handwritten data sets show that the proposed method has better overall performance than Otsu,Wolf,Niblack,Sauvola,Singh and Howe.

Key words: Image processing, Handwritten document image, Binarization, Background estimate, Binary integration

CLC Number: 

  • TP391
[1]SULAIMAN A,OMAR K,NASRUDIN M F.Degraded historical document binarization:A review on issues,challenges,techniques,and future directions[J].Journal of Imaging,2019,5(4):48.
[2]OTSU N.A Threshold Selection Method from Gray-Level Histograms[J].IEEE Transactions on Systems Man & Cyberne-tics,1979,9(1):62-66.
[3]NIBLACK W.An Introduction to Digital Image Processing[M].Englewood Cliffs,NJ:Prentice-Hall,Inc,1986:115-116.
[4]SAUVOLA J,PIETIKÄINEN M.Adaptive document image binarization[J].Pattern Recognition,2000,33(2):225-236.
[5]HADJADJ Z,MEZIANE A,CHERFA Y,et al.ISauvola:Im-proved Sauvola’s algorithm for document image binarization[C]//International Conference on Image Analysis and Recognition.Póvoa de Varzim,Portugal:Springer,2016:737-745.
[6]ZHU D L,YANG D G,HU R,et al.Adaptive Multi-levelThreshold Binaryzation Method for Optical Character Recognition in Mobile Environment[J].Computer Science,2019,46(8):315-320.
[7]HOWE N R.Document binarization with automatic parameter tuning[J].International Journal on Document Analysis and Reco-gnition,2013,16(3):247-258.
[8]BHOWMIK S,SARKAR R,DAS B,et al.GiB:a Game theory In-spired Binarization technique for degraded document images[J].IEEE Transactions on Image Processing,2018,28(3):1443-1455.
[9]BARDOZZO F,DE LA OSA B,HORANSKÁ L′,et al.Sugeno integral generalization applied to improve adaptive image binarization[J].Information Fusion,2021,68:37-45.
[10]RANI U,KAUR A,JOSAN G.A New Contrast Based Degraded Document Image Binarization[J].Cognitive Computing in Human Cognition,2020,17(1):83-90.
[11]SUN G D,XU Y,XU L,et al.Binarization method of instrument image with uneven illumination and ghosting[J].Journal of Applied Optics,2020,41(1):74-78.
[12]WESTPHAL F,LAVESSON N,GRAHN H.Document image binarization using recurrent neural networks[C]//2018 13th IAPR International Workshop on Document Analysis Systems(DAS).Vienna,Austria:IEEE,2018:263-268.
[13]KANG S,IWANA B K,UCHIDA S.Complex image processing with less data—Document image binarization by integrating multiple pre-trained U-Net modules[J].Pattern Recognition,2021,109:107577.
[14]CASTELLANOS F J,GALLEGO A J,CALVO-ZARAGOZA J.Unsupervised neural domain adaptation for document image binarization[J].Pattern Recognition,2021,119(2):108099.
[15]TENSMEYER C,MARTINEZ T.Historical document image binarization:A review[J].SN Computer Science,2020,1(3):1-26.
[16]MUSTAFA W A,KADER M M M A.Binarization of document images:A comprehensive review[J].Journal of Physics:Confe-rence Series,2018,1019(1):012023.
[17]XU H Y,MA L L,WU J,et al.Document image binarization based on background estimation and edge detection[J].Compu-ter Applications and Software,2014,31(8):196-200.
[18]HEDJAM R,NAFCHI H Z,KALACSKA M,et al.Influence of color-to-gray conversion on the performance of document image binarization:Toward a novel optimization problem[J].IEEE Transactions on Image Processing,2015,24(11):3637-3651.
[19]WOLD S,ESBENSEN K,GELADI P.Principal component ana-lysis[J].Chemometrics and Intelligent Laboratory Systems,1987,2(1/2/3):37-52.
[20]ITU-R R,BT.Studio encoding parameters of digital television for standard 4:3 and wide-screen 16:9 aspect ratios[S].Geneva,Switzerland:ITU,2011.
[21]PRATIKAKIS I,GATOS B,NTIROGIANNIS K.ICDAR 2013 Document Image Binarization Contest(DIBCO 2013)[C]//2013 12th International Conference on Document Analysis and Recognition.Washington,DC,USA:IEEE,2013:1471-1476.
[22]PRATIKAKIS I,ZAGORIS K,BARLAS G,et al.ICFHR2016 Handwritten Document Image Binarization Contest(H-DIBCO 2016)[C]//2016 15th International Conference on Frontiers in Handwriting Recognition(ICFHR).Shenzhen,China:IEEE,2016:619-623.
[23]WOLF C,JOLION J M.Extraction and Recognition of Artificial Text in Multimedia Documents[J].Formal Pattern Analysis & Applications,2004,6(4):309-326.
[24]SINGH O I,JAMES O,SINAM T,et al.Local Contrast andMean based Thresholding Technique in Image Binarization[J].International Journal of Computer Applications,2013,51(6):4-10.
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