Forensic Document Examination: Who Is the Writer?

This work presents a baseline system to automatic handwriting identification based only on graphometric features. Initially a set composed of 12 features was presented and its extraction process demonstrated. In order to evaluate the efficiency of these features, a selection process was applied, and a smaller group composed only of 4 features (GS = Goodness Subset) present the best writer identification rates. Experiments were conducted in order to evaluate the performance, individually and in group, of the graphometric features; and to identify the number of writers that significantly affect the accuracy of the system. The accuracy of the system applied to 100 different writers taking account the GS features set were 84% (TOP1), 96% (TOP5) and 98% (TOP10). These results are comparable to others in the literature on graphometric features. It can be observed that gradually the relation between the number of writers and accuracy is stabilized, and with 200 writers the results are maintained.

[1]  Zhenyu He,et al.  Writer identification of Chinese handwriting documents using hidden Markov tree model , 2008, Pattern Recognit..

[2]  Graham Leedham,et al.  Extraction and analysis of forensic document examiner features used for writer identification , 2007, Pattern Recognit..

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

[4]  Lambert Schomaker,et al.  Using codebooks of fragmented connected-component contours in forensic and historic writer identification , 2007, Pattern Recognit. Lett..

[5]  Thierry Paquet,et al.  A writer identification and verification system , 2005, Pattern Recognit. Lett..

[6]  Réjean Plamondon,et al.  Automatic signature verification and writer identification - the state of the art , 1989, Pattern Recognit..

[7]  Mohsen Ebrahimi Moghaddam,et al.  A text-independent Persian writer identification based on feature relation graph (FRG) , 2010, Pattern Recognit..

[8]  Katrin Franke,et al.  Ink-deposition model: the relation of writing and ink deposition processes , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[9]  Daniel P. Lopresti,et al.  The Impact of Ruling Lines on Writer Identification , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.

[10]  Luiz Eduardo Soares de Oliveira,et al.  Writer verification using texture-based features , 2011, International Journal on Document Analysis and Recognition (IJDAR).

[11]  Flávio Bortolozzi,et al.  Brazilian forensic letter database , 2008 .

[12]  Lambert Schomaker,et al.  Text-Independent Writer Identification and Verification on Offline Arabic Handwriting , 2007 .

[13]  Carla E. Brodley,et al.  Feature Subset Selection and Order Identification for Unsupervised Learning , 2000, ICML.

[14]  Lambert Schomaker,et al.  Writer identification and verification , 2008 .

[15]  Cinthia Obladen de Almendra Freitas,et al.  Feature Selection for Forensic Handwriting Identification , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[16]  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.

[17]  Edgardo Manuel Felipe Riverón,et al.  A supervised algorithm with a new differentiated-weighting scheme for identifying the author of a handwritten text , 2011, Pattern Recognit. Lett..

[18]  Horst Bunke,et al.  Off-line handwriting identification using HMM based recognizers , 2004, ICPR 2004.

[19]  Tieniu Tan,et al.  Writer identification based on handwriting , 1998 .

[20]  Jianying Hu,et al.  Writer independent on-line handwriting recognition using an HMM approach , 2000, Pattern Recognit..

[21]  Louis Vuurpijl,et al.  Writer identification by means of explainable features: shapes of loops and lead-in strokes , 2007 .

[22]  Vassilis Anastassopoulos,et al.  Morphological waveform coding for writer identification , 2000, Pattern Recognit..