Offline Writer Identification Based on CLBP and VLBP

Abstract

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.

Writer identification using textural features

“…Several other studies have been proposed based on several textural descriptors such as , VLBP [1], edge-hinge [4], Run length [4], Contour-direction [2], Contour-hinge [2], edge-direction [4], CLBP [1] ...…”

Writer identification using VLAD Encoding of the Histogram of Gradient Angle Distribution

“…In addition to the systems mentioned above, several methods have been proposed based on other local descriptors, such as Run length [12], edge-hinge [12], edge-direction [12], Contour-hinge [8], Contourdirection [8], CLBP [1], VLBP [1] ... The introduction of neural networks has made it possible to achieve good performance [24,25,13,27,10,18].…”