Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Writer identification approach based on bag of words with OBI features
AU - Durou, Amal
AU - Aref, Ibrahim
AU - Al-Maadeed, Somaya
AU - Bouridane, Ahmed
AU - Benkhelifa, Elhadj
PY - 2019/3/31
Y1 - 2019/3/31
N2 - Handwriter identification aims to simplify the task of forensic experts by providing them with semi-automated tools in order to enable them to narrow down the search to determine the final identification of an unknown handwritten sample. An identification algorithm aims to produce a list of predicted writers of the unknown handwritten sample ranked in terms of confidence measure metrics for use by the forensic expert will make the final decision.Most existing handwriter identification systems use either statistical or model-based approaches. To further improve the performances this paper proposes to deploy a combination of both approaches using Oriented Basic Image features and the concept of graphemes codebook. To reduce the resulting high dimensionality of the feature vector a Kernel Principal Component Analysis has been used. To gauge the effectiveness of the proposed method a performance analysis, using IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting, has been carried out. The results obtained achieved an accuracy of 96% thus demonstrating its superiority when compared against similar techniques.
AB - Handwriter identification aims to simplify the task of forensic experts by providing them with semi-automated tools in order to enable them to narrow down the search to determine the final identification of an unknown handwritten sample. An identification algorithm aims to produce a list of predicted writers of the unknown handwritten sample ranked in terms of confidence measure metrics for use by the forensic expert will make the final decision.Most existing handwriter identification systems use either statistical or model-based approaches. To further improve the performances this paper proposes to deploy a combination of both approaches using Oriented Basic Image features and the concept of graphemes codebook. To reduce the resulting high dimensionality of the feature vector a Kernel Principal Component Analysis has been used. To gauge the effectiveness of the proposed method a performance analysis, using IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting, has been carried out. The results obtained achieved an accuracy of 96% thus demonstrating its superiority when compared against similar techniques.
KW - Writer identification
KW - Oriented basic image
KW - Kernel principal component analysis
KW - Graphemes
KW - Text independent classification
UR - https://www.researchgate.net/publication/320341379_Writer_identification_approach_based_on_bag_of_words_with_OBI_features
U2 - 10.1016/j.ipm.2017.09.005
DO - 10.1016/j.ipm.2017.09.005
M3 - Journal article
VL - 56
SP - 354
EP - 366
JO - Information Processing & Management
JF - Information Processing & Management
IS - 2
ER -