Computer Science ›› 2019, Vol. 46 ›› Issue (8): 315-320.doi: 10.11896/j.issn.1002-137X.2019.08.052

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Adaptive Multi-level Threshold Binaryzation Method for Optical Character Recognition in Mobile Environment

ZHU De-li1, YANG De-gang1, HU Rong2, WAN Hui1   

  1. (College of Computer and Information Science,Chongqing Normal University,Chongqing 401331,China)1
    (College of Computer and Information Science,Southwest University,Chongqing 400715,China)2
  • Received:2018-07-04 Online:2019-08-15 Published:2019-08-15

Abstract: In order to solve the problem of poor binaryzation quality caused by uneven illumination and uncontrollable environment in OCR applications of mobile terminals,this paper proposed an adaptive multi-level threshold binaryzation method based on integral graph.First,a specific sliding window is set by focusing on the points to be calculated.The normal threshold is the mean value of the sliding window where the current point is located.The two front sliding windows are weighted according to the Gauss function,and then the relaxation factor is obtained according to the weights.The relaxation threshold of pixels are obtained based on the evaluation of the relaxation factor and illumination condition.Experiments were carried out in typical mobile environments such as irregular shadows,multi-level illumination and linear light changes.Lenovo ZUK Z2 Pro is used as the test equipment.The average recall of the algorithm is 95.5% and the average accuracy is 91%.The recognition accuracy of this algorithm is 96.8%,98.2% and 93.2% respectively in the environment of irregular shadow,multilevel illumination and linear light change.The result shows that the proposed algorithm has strong robustness and adaptability,and can meet the requirement of image preprocessing in the OCR application of mobile terminal

Key words: Adaptive binaryzation, Image processing, Integral graph, Mobile terminal, Optical character recognition

CLC Number: 

  • TP391
[1]ISMAIL S M,ABDULLAH S N H S,FAUZI F.Statistical bina- rization techniques for document image analysis[J].Journal of Computer Science,2018,14(1):23-36.
[2]BATAINEH B,ABDULLAH S N,OMAR K.Adaptive binarization method for degraded document images based on surface contrast variation[J].Pattern Analysis & Applications,2015,20:1-14.
[3]DUAN S L,ZHU F,YAN X.Research of Multi window binaryzation algorithm [J].Computer Engineering and Application,2017,53(17):212-217.(in Chinese) 段锁林,朱方,严翔.多窗口图像二值化算法研究[J].计算机工程与应用,2017,53(17):212-217.
[4]LI Z,LI G Y.Research on the binaryzation of power meter in complex lighting environment [J].microcomputers and applications,2017,36(15):45-48.(in Chinese) 李真,李功燕.复杂光照环境下电力仪表的二值化研究[J].微型机与应用,2017,36(15):45-48.
[5]XIONF W,WANG X R,FENG C.Document image binaryzation based on background estimation and energy minimization [J].Computer Application 2018,38(3):1-8.(in Chinese) 熊炜,王鑫睿,冯川.基于背景估计和能量最小化的文档图像二值化[J].计算机应用2018,38(3):1-8.
[6]WU R,HUANG J H,TANG J L.Binaryzation method of text image based on gray histogram and spectral clustering [J].Journal of Electronic and Information,2009,31(10):2460-2464.(in Chinese) 吴锐,黄剑华,唐降龙.基于灰度直方图和谱聚类的文本图像二值化方法[J].电子与信息学报,2009,31(10):2460-2464.
[7]PAN M S,RONG Q S.Image fusion binaryzation method based on SOFM neural network [J].Optical Precision Engineering,2007(3):401-406.(in Chinese) 潘梅森,荣秋生.基于SOFM神经网络的图像融合二值化方法[J].光学精密工程,2007(3):401-406.
[8]VO G D,PARI C.Robust Regression For image binarization under heavy noise and nonuniform background[J].Pattern Recognition,2018,81(2):224-239.
[9]SEZGIN M,SANKUR B.Survey over image thresholding techniques and quantitative performance evaluation[J].Journal of Electronic Imaging,2004,13(1):146-168.
[10]OTSU N.A Threshold Selection Method from Gray-Level Histograms[J].IEEE Transactions on Systems,Man,and Cybernetics,1979,9(1):62-66.
[11]ZHANG Y,WU L.Fast Document Image Binarization based on an improved adaptive Otsu’s method and destination word accumulation[J].Journal of Computational Information Systems,2011,7(6):1886-1892.
[12]MICHALAK H,OKARMA K.Fast Adaptive Image Binariza- tion Using the Region Based Approach[C]∥Computer Science On-line Conference.Cham:Springer,2018:79-90.
[13]NASRI M,HOSSEIN-NEJAD Z,HOSSEINI-ZAHMATKESH P.Document Image Binarization Based on Combination of Globaland Local Thresholding Methods[J].International Journal of Image & Graphics,2018,18(2):179-186.
[14]NTIROGIANNIS K,GATOS B.Combined approach for the binarization of handwritten document images[J].Pattern Recognition Letters,2014,35:3-15.
[15]HOWE N R.ERRATUM.Document binarization with automatic parameter tuning[J].International Journal on Document Analysis & Recognition,2013,16(3):247-258.
[16]SU B,LU S,TAN C L.Robust document image binarization technique for degraded document images[J].IEEE Transactions on Image Processing,2013,22(4):1408-1417.
[17]LU D,HUANG X,SUI L X.Binarization of degraded document images based on contrast enhancement[J].International Journal on Document Analysis & Recognition,2018,21(1/2):123-135.
[18]BRADLEY D,ROTH G.Adaptive Thresholding using the integral graph[J].Journal of Graphics Gpu & Game Tools,2007,12(2):13-21.
[19]GATOS B,PRATIKAKIS I,PERANTONIS S.Adaptive de- graded document image binarization[J].Pattern Recognition,2006,39(3):317-327.
[1] YU Bin, LI Xue-hua, PAN Chun-yu, LI Na. Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning [J]. Computer Science, 2022, 49(7): 248-253.
[2] LAI Teng-fei, ZHOU Hai-yang, YU Fei-hong. Real-time Extend Depth of Field Algorithm for Video Processing [J]. Computer Science, 2022, 49(6A): 314-318.
[3] LIU Wei-ye, LU Hui-min, LI Yu-peng, MA Ning. Survey on Finger Vein Recognition Research [J]. Computer Science, 2022, 49(6A): 1-11.
[4] YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515.
[5] ZHAN Rui, LEI Yin-jie, CHEN Xun-min, YE Shu-han. Street Scene Change Detection Based on Multiple Difference Features Network [J]. Computer Science, 2021, 48(2): 142-147.
[6] ZHANG Yu-long, WANG Qiang, CHEN Ming-kang, SUN Jing-tao. Survey of Intelligent Rain Removal Algorithms for Cloud-IoT Systems [J]. Computer Science, 2021, 48(12): 231-242.
[7] KOU Xi-chao, ZHANG Hong-rui, FENG Jie, ZHENG Ya-yu. Distortion Correction Algorithm for Complex Document Image Based on Multi-level TextDetection [J]. Computer Science, 2021, 48(12): 249-255.
[8] YAO Nan, ZHANG Zheng. Scar Area Calculation Based on 3D Image [J]. Computer Science, 2021, 48(11A): 308-313.
[9] FENG Yi-fan, ZHAO Xue-qing, SHI Xin, YANG Kun. Light Superposition-based Color Constancy Computational Method [J]. Computer Science, 2021, 48(11A): 386-390.
[10] SONG Yi-yan, TANG Dong-lin, WU Xu-long, ZHOU Li, QIN Bei-xuan. Study on Digital Tube Image Reading Combining Improved Threading Method with HOG+SVM Method [J]. Computer Science, 2021, 48(11A): 396-399.
[11] XIE Hai-ping, LI Gao-yuan, YANG Hai-tao, ZHAO Hong-li. Classification Research of Remote Sensing Image Based on Super Resolution Reconstruction [J]. Computer Science, 2021, 48(11A): 424-428.
[12] CAI Yu-xin, TANG Zhi-wei, ZHAO Bo, YANG Ming and WU Yu-fei. Accelerated Software System Based on Embedded Multicore DSP [J]. Computer Science, 2020, 47(6A): 622-625.
[13] MA Hong. Fusion Localization Algorithm of Visual Aided BDS Mobile Robot Based on 5G [J]. Computer Science, 2020, 47(6A): 631-633.
[14] SONG Ya-fei, CHEN Yu-zhang, SHEN Jun-feng and ZENG Zhang-fan. Underwater Image Reconstruction Based on Improved Residual Network [J]. Computer Science, 2020, 47(6A): 500-504.
[15] MIAO Yi, ZHAO Zeng-shun, YANG Yu-lu, XU Ning, YANG Hao-ran, SUN Qian. Survey of Image Captioning Methods [J]. Computer Science, 2020, 47(12): 149-160.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!