SVM-based writer retrieval system in handwritten document images

Digital libraries include huge amount of information that are continuously increasing with the need of storing various kinds of handwritten documents such as administrative forms, and cultural heritage manuscripts. Therefore, new emerging techniques such as writer retrieval are used to facilitate information extraction from archived documents. Basically, a writer retrieval system is composed of two main steps that are feature generation and dissimilarity measure. To achieve a robust retrieval, we propose the use of an SVM classifier trained to automatically separate intra-writer features from inter-writer features. For feature generation, we investigate the effectiveness of the Histogram of Oriented Gradients, Gradient Local Binary Patterns, and Local Difference Features. Experiments are conducted on three benchmark datasets. The results obtained evince a satisfactory performance of SVM, which can give better results than the state of the art.

[1]  Hassiba Nemmour,et al.  New gradient features for off-line handwritten signature verification , 2015, 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA).

[2]  Horst Bunke,et al.  The IAM-database: an English sentence database for offline handwriting recognition , 2002, International Journal on Document Analysis and Recognition.

[3]  Basilios Gatos,et al.  ICDAR 2011 Writer Identification Contest , 2011, 2011 International Conference on Document Analysis and Recognition.

[4]  Noor A. Jebril,et al.  Recognition of Handwritten Arabic Characters using Histograms of Oriented Gradient (HOG) , 2018 .

[5]  Umapada Pal,et al.  Local Binary Pattern for Word Spotting in Handwritten Historical Document , 2016, S+SSPR.

[6]  Mustafa Berkay Yilmaz,et al.  Offline signature verification using classifier combination of HOG and LBP features , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[7]  Robert Sablatnig,et al.  Writer Identification and Writer Retrieval Using the Fisher Vector on Visual Vocabularies , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[8]  Zhenhua Guo,et al.  Face recognition using part-based dense sampling local features , 2016, Neurocomputing.

[9]  Hassiba Nemmour,et al.  Score Level Fusion for Improving Writer Retrieval in Handwritten Document Databases , 2020, 020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP).

[10]  Laurence Likforman-Sulem,et al.  Writer Retrieval - Exploration of a Novel Biometric Scenario Using Perceptual Features Derived from Script Orientation , 2011, 2011 International Conference on Document Analysis and Recognition.

[11]  Hassiba Nemmour,et al.  Evaluation of Gradient Descriptors and Dissimilarity Learning for Writer Retrieval , 2018, 2018 Eighth International Conference on Information Science and Technology (ICIST).

[12]  Mohammad Alshayeb,et al.  KHATT: Arabic Offline Handwritten Text Database , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[13]  Hassiba Nemmour,et al.  Writer Retrieval Using Histogram Of Templates Features and SVM , 2017 .

[14]  Robert Sablatnig,et al.  CVL-DataBase: An Off-Line Database for Writer Retrieval, Writer Identification and Word Spotting , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[15]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[16]  Robert Sablatnig,et al.  Writer Identification and Retrieval Using a Convolutional Neural Network , 2015, CAIP.

[17]  M. S. Shirdhonkar,et al.  Writer Based Handwritten Document Image Retrieval Using Contourlet Transform , 2011 .

[18]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[19]  A. Taleb-Ahmed,et al.  Multimodal depth and intensity face verification approach using LBP, SLF, BSIF, and LPQ local features fusion , 2015, Pattern Recognition and Image Analysis.

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

[21]  Hassiba Nemmour,et al.  Improved Multi-Scale Local Difference Features for Off-Line Handwritten Signature Verification , 2020, 020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP).

[22]  Hassiba Nemmour,et al.  Age, gender and handedness prediction from handwriting using gradient features , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[23]  Hassiba Nemmour,et al.  Robust soft-biometrics prediction from off-line handwriting analysis , 2016, Appl. Soft Comput..

[24]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[25]  Labiba Souici-Meslati,et al.  Text-independent writer recognition using multi-script handwritten texts , 2013, Pattern Recognit. Lett..

[26]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[27]  Robert Sablatnig,et al.  Writer Retrieval and Writer Identification Using Local Features , 2012, 2012 10th IAPR International Workshop on Document Analysis Systems.

[28]  Hassiba Nemmour,et al.  Multiple writer retrieval systems based on language independent dissimilarity learning , 2020, Expert Syst. Appl..

[29]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[30]  Satoshi Goto,et al.  Gradient Local Binary Patterns for human detection , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[31]  Ching Y. Suen,et al.  An Enhanced Histogram of Oriented Gradient Descriptor for Numismatic Applications , 2018, Pattern Recognition and Image Analysis.