Feature learning and encoding for multi-script writer identification

Semma, Abdelillah; Hannad, Yaâcoub; Siddiqi, Imran; Lazrak, Said; Kettani, Mohamed El Youssfi El

doi:10.1007/s10032-022-00394-8

Cited by 8 publications

(1 citation statement)

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“…The feature extraction technique identifies the signature’s texture features and handwriting 8 . The support vector machine (SVM) model is used to verify handwriting signatures presented in the database and detects the important texture features 9 . Local binary patterns (LBP) and greyscale levels of signatures are verified based on certain functions 10 .…”

Section: Introductionmentioning

confidence: 99%

Handwriting identification and verification using artificial intelligence-assisted textural features

Zhao,

2023

Sci Rep

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Intelligent process control and automation systems require verification authentication through digital or handwritten signatures. Digital copies of handwritten signatures have different pixel intensities and spatial variations due to the factors of the surface, writing object, etc. On the verge of this fluctuating drawback for control systems, this manuscript introduces a Spatial Variation-dependent Verification (SVV) scheme using textural features (TF). The handwritten and digital signatures are first verified for their pixel intensities for identification point detection. This identification point varies with the signature’s pattern, region, and texture. The identified point is spatially mapped with the digital signature for verifying the textural feature matching. The textural features are extracted between two successive identification points to prevent cumulative false positives. A convolution neural network aids this process for layered analysis. The first layer is responsible for generating new identification points, and the second layer is responsible for selecting the maximum matching feature for varying intensity. This is non-recurrent for the different textures exhibited as the false factor cuts down the iterated verification. Therefore, the maximum matching features are used for verifying the signatures without high false positives. The proposed scheme’s performance is verified using accuracy, precision, texture detection, false positives, and verification time.

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Section: Introductionmentioning

confidence: 99%