Historic handwritten manuscript binarisation using whale optimisation

Preserving the content of historic handwritten manuscripts is important for a variety of reasons. On the other hand, digital libraries are rapidly expanding and thus facilitate to store this information directly in digital form. For digitising text documents, a crucial step is to binarise the captured images to separate the text from the background. In this paper, we propose an effective approach for binarisation of handwritten Arabic manuscripts which employs a whale optimisation algorithm, incorporating a fuzzy c-means objective function, to obtain optimal thresholds. Experimental results confirm the effectiveness of the proposed approach compared to earlier methods.

[1]  William A. Watkins,et al.  Aerial Observation of Feeding Behavior in Four Baleen Whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaeangliae, and Balaenoptera physalus , 1979 .

[2]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[3]  Ioannis Pratikakis,et al.  DIBCO 2009: document image binarization contest , 2011, International Journal on Document Analysis and Recognition (IJDAR).

[4]  Gerald Schaefer,et al.  A binarization algorithm for historical arabic manuscript images using a neutrosophic approach , 2014, 2014 9th International Conference on Computer Engineering & Systems (ICCES).

[5]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[6]  Aboul Ella Hassanien,et al.  Artificial bee colony optimizer for historical Arabic manuscript images binarization , 2015, 2015 11th International Computer Engineering Conference (ICENCO).

[7]  Aboul Ella Hassanien,et al.  A Novel Hybrid Binarization Technique for Images of Historical Arabic Manuscripts , 2015 .

[8]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[9]  Haiping Lu,et al.  Distance-reciprocal distortion measure for binary document images , 2004, IEEE Signal Processing Letters.

[10]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

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

[12]  Rafael Dueire Lins,et al.  ICFHR 2010 Contest: Quantitative Evaluation of Binarization Algorithms , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.

[13]  Ioannis Pratikakis,et al.  ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[14]  Xin Chen,et al.  Parallel nonparametric binarization for degraded document images , 2016, Neurocomputing.

[15]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[16]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[17]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[18]  Ioannis Pratikakis,et al.  Performance Evaluation Methodology for Historical Document Image Binarization , 2013, IEEE Transactions on Image Processing.

[19]  Wayne Niblack,et al.  An introduction to digital image processing , 1986 .

[20]  G. Zack,et al.  Automatic measurement of sister chromatid exchange frequency. , 1977, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.