Discriminating Features for Writer Identification

This paper investigates highly discriminating features for writer identification for off-line handwritten text lines and passages. Five categories of features are tested: slant and slant energy, skew, pixel distribution, curvature, and entropy. Four experiments are run utilizing the IAM Handwriting Database and the ICDAR 2011 Writer Identification Contest dataset: the first, on 10 writers from the IAM dataset, the second, on 50 writers from the IAM dataset, the third, on 100 writers from the IAM dataset, and the fourth, strictly following the methodology of the 2011 ICDAR Writer Identification Contest. When compared to the other methodologies tested in the ICDAR competition, ours ranked fourth out of nine. These features support high recognition rates and are competitive with other state of the art methods for writer identification.

[1]  Horst Bunke,et al.  Writer identification using text line based features , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

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

[3]  Somaya Al-Máadeed,et al.  Writer identification of Arabic handwriting documents using grapheme features , 2008, 2008 IEEE/ACS International Conference on Computer Systems and Applications.

[4]  Djeddi Chawki,et al.  A texture based approach for Arabic writer identification and verification , 2010, 2010 International Conference on Machine and Web Intelligence.

[5]  Nicole Vincent,et al.  A Set of Chain Code Based Features for Writer Recognition , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[6]  Xin Li,et al.  An Improved Method Based on Weighted Grid Micro-structure Feature for Text-Independent Writer Recognition , 2011, 2011 International Conference on Document Analysis and Recognition.

[7]  Lambert Schomaker,et al.  Automatic writer identification using connected-component contours and edge-based features of uppercase Western script , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Somaya Al-Máadeed,et al.  Writer identification using edge-based directional probability distribution features for arabic words , 2008, 2008 IEEE/ACS International Conference on Computer Systems and Applications.

[9]  Sung-Hyuk Cha,et al.  Individuality of handwriting: a validation study , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[10]  Graham Leedham,et al.  Writer identification using innovative binarised features of handwritten numerals , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[11]  Louis Vuurpijl,et al.  Writer identification using edge-based directional features , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[12]  Mary Idicula Sumam,et al.  A Survey on Writer Identification Schemes , 2011 .

[13]  Henry S. Baird,et al.  The skew angle of printed documents , 1995 .

[14]  Horst Bunke,et al.  A Set of Novel Features for Writer Identification , 2003, AVBPA.

[15]  David Antin,et al.  100 Great Problems of Elementary Mathematics , 1965 .

[16]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[17]  Lambert Schomaker,et al.  Text-Independent Writer Identification and Verification Using Textural and Allographic Features , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[19]  David S. Doermann,et al.  Offline Writer Identification Using K-Adjacent Segments , 2011, 2011 International Conference on Document Analysis and Recognition.

[20]  Lawrence O'Gorman,et al.  Practical Algorithms for Image Analysis with CD-ROM , 2008 .