Writer identification from handwriting is still considered to be challenging task due to homogeneous vision comparing writer of handwritten documents. This paper presents a new method based on two LBPs kinds: Complete Local Binary Patterns (CLBP) and Local Binary Pattern Variance (LBPV) for extracting the features from handwriting documents. The feature vector is then normalized using Probability Density Function (PDF). Classifications are based on the minimization of a similarity criteria based on a distance between two features vectors. A series of evaluations using different combinations of distances metrics are realized high identification rates which are compared with the methods that are participated in the ICDAR 2013 competition.

Identifying the writer of a handwritten document has remained an interesting pattern classification problem for document examiners, forensic experts, and paleographers. While mature identification systems have been developed for handwriting in contemporary documents, the problem remains challenging from the viewpoint of historical manuscripts. Design and development of expert systems that can identify the writer of a questioned manuscript or retrieve samples belonging to a given writer can greatly help the paleographers in their practices. In this context, the current study exploits the textural information in handwriting to characterize writer from historical documents. More specifically, we employ oBIF(oriented Basic Image Features) and hinge features and introduce a novel moment-based matching method to compare the feature vectors extracted from writing samples. Classification is based on minimization of a similarity criterion using the proposed moment distance. A comprehensive series of experiments using the International Conference on Document Analysis and Recognition 2017 historical writer identification dataset reported promising results and validated the ideas put forward in this study.

Identification of writers from images of handwriting is an interesting research problem in the handwriting recognition community. Application of image analysis and machine learning techniques to this problem allows development of computerised solutions which can facilitate forensic experts in reducing the search space against a questioned document. This article investigates the effectiveness of textural measures in characterising the writer of a handwritten document. A novel descriptor by crossing the local binary patterns (LBP) with different configurations that allows capturing the local textural information in handwriting using a column histogram is introduced. The representation is enriched with the oriented Basic Image Features (oBIFs) column histogram. Support vector machine (SVM) is employed as the classifier, and the experimental study is carried out on five different datasets in single as well as multi‐script evaluation scenarios. Multi‐script evaluations allow evaluating the hypothesis that writers share common characteristics across multiple scripts and the reported results validate the effectiveness of textural measures in capturing this script‐independent, writer‐specific information.

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