Author: Abbas, Faycel; Gattal, Abdeljalil; Djeddi, Chawki; Bensefia, Ameur; Jamil, Akhtar; Saoudi, Kamel
Title: Offline Writer Identification Based on CLBP and VLBP Cord-id: lxjfcw3j Document date: 2021_2_22
ID: lxjfcw3j
Snippet: 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
Document: 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.
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