Statistical Binarization Techniques for Document Image Analysis

Binarization is an important process in image enhancement and analysis. Currently, numerous binarization techniques have been reported in the literature. These binarization methods produce binary images from color or gray-level images. This article highlights an extensive review on various binarization approaches which are also referred to as thresholding methods. These methods are grouped into seven categories according to the employed features and techniques: histogram shape-based, clustering-based, entropy-based, object-attribute-based, spatial, local and hybrid methods. Most active binarization researchers exploit several initial information from the source image such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation and local gray level surface with a special attention to statistical information description features of image used in recent thresholding techniques.

[1]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[2]  Matti Pietikäinen,et al.  Adaptive document binarization , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[3]  Marzuki Khalid,et al.  Determining adaptive thresholds for image segmentation for a license plate recognition system , 2016 .

[4]  Cheng-Huang Tung,et al.  Binarization of uneven-lighting image by maximizing boundary connectivity , 2017 .

[5]  Liansheng Wang,et al.  Broken and degraded document images binarization , 2017, Neurocomputing.

[6]  Anton Satria Prabuwono,et al.  Ensemble of steerable local neighbourhood grey-level information for binarization , 2017, Pattern Recognit. Lett..

[7]  Carlos A. B. Mello,et al.  A local thresholding algorithm for images of handwritten historical documents , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[8]  Khairuddin Omar,et al.  Adaptive binarization method for degraded document images based on surface contrast variation , 2015, Pattern Analysis and Applications.

[9]  Mohamed Cheriet,et al.  Ancient degraded document image binarization based on texture features , 2013, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA).

[10]  Shanq-Jang Ruan,et al.  Adaptive thresholding algorithm: Efficient computation technique based on intelligent block detection for degraded document images , 2010, Pattern Recognit..

[11]  Khairuddin Omar,et al.  A novel statistical feature extraction method for textual images: Optical font recognition , 2012, Expert Syst. Appl..

[12]  Efstathios Stamatatos,et al.  Adaptive Binarization of Historical Document Images , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[13]  Ioannis Pratikakis,et al.  Efficient Binarization of Historical and Degraded Document Images , 2008, 2008 The Eighth IAPR International Workshop on Document Analysis Systems.

[14]  Nicolae Tapus,et al.  Local Thresholding Algorithm Based on Variable Window Size Statistics , 2012 .

[15]  Sakinah Ali Pitchay,et al.  COMPOUND BINARIZATION FOR DEGRADED DOCUMENT IMAGES , 2015 .

[16]  Siti Norul Huda Sheikh Abdullah,et al.  Novel Binarization Method for Enhancing Ancient and Historical Manuscript Images , 2014, MICAI.

[17]  B. Kapralos,et al.  I An Introduction to Digital Image Processing , 2022 .

[18]  Nikhil R. Pal,et al.  On minimum cross-entropy thresholding , 1996, Pattern Recognit..

[19]  Sudipta Roy,et al.  A New Local Adaptive Thresholding Technique in Binarization , 2012, ArXiv.

[20]  Tien-Ying Kuo,et al.  A novel image binarization method using hybrid thresholding , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[21]  Ioannis Pratikakis,et al.  ICDAR 2013 Document Image Binarization Contest (DIBCO 2013) , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[22]  Shijian Lu,et al.  Combination of Document Image Binarization Techniques , 2011, 2011 International Conference on Document Analysis and Recognition.

[23]  Zhang Huayu,et al.  Binarization of degraded document image based on contrast enhancement , 2016, 2016 35th Chinese Control Conference (CCC).

[24]  H. D. Cheng,et al.  Threshold selection based on fuzzy c-partition entropy approach , 1998, Pattern Recognit..

[25]  Sitti Rachmawati Yahya,et al.  Adaptive Binarization Method for Enhancing Ancient Malay Manuscript Images , 2011, Australasian Conference on Artificial Intelligence.

[26]  Chung-Chih Lin,et al.  Segmentation of mitochondria in fluorescence micrographs by SVM , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).

[27]  Nicole Vincent,et al.  Comparison of Niblack inspired binarization methods for ancient documents , 2009, Electronic Imaging.

[28]  Swati A. Patil,et al.  A Review of Image Binarization Techniques for Ancient Degraded Documents , 2015 .

[29]  Ioannis Pratikakis,et al.  A combined approach for the binarization of handwritten document images , 2014, Pattern Recognit. Lett..

[30]  Shijian Lu,et al.  Document image binarization using background estimation and stroke edges , 2010, International Journal on Document Analysis and Recognition (IJDAR).

[31]  Thierry Géraud,et al.  Efficient multiscale Sauvola’s binarization , 2013, International Journal on Document Analysis and Recognition (IJDAR).

[32]  Shahnorbanun Sahran,et al.  PEAK SIGNAL-TO-NOISE RATIO BASED ON THRESHOLD METHOD FOR IMAGE SEGMENTATION , 2013 .

[33]  Nikolaos Mitianoudis,et al.  Document image binarization using local features and Gaussian mixture modeling , 2015, Image Vis. Comput..

[34]  Khairuddin Omar,et al.  An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows , 2011, Pattern Recognit. Lett..

[35]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[36]  Mohamed Cheriet,et al.  AdOtsu: An adaptive and parameterless generalization of Otsu's method for document image binarization , 2012, Pattern Recognit..