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Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

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Handwriting based writer recognition using implicit shape codebook.

Akram Bennour¹, Chawki Djeddi¹, Abdeljalil Gattal¹

¹Larbi Tebessi University, Tebessa, Algeria.

Forensic Science International

|June 2, 2019

Summary

This study introduces an implicit shape codebook technique for writer recognition from handwriting images. The method effectively characterizes writers by analyzing patterns in their unique handwriting for forensic and neuropsychological applications.

Keywords:

Agglomerative clustering Harris key-point detector Implicit shape codebook SVM Writer identification and verification

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Area of Science:

Computer Vision
Pattern Recognition
Biometrics

Background:

Writer characterization from handwriting images is crucial for forensics, paleography, and neuropsychology.
Existing methods require robust feature extraction for accurate writer identification.

Purpose of the Study:

To evaluate an implicit shape codebook technique for writer recognition.
To assess the effectiveness of this method in text-dependent and text-independent scenarios.

Main Methods:

Key points in handwriting are identified.
Patches around key points are clustered to form an implicit shape codebook.
Writer characterization is based on the probability distribution of codebook patterns.

Main Results:

The implicit shape codebook technique demonstrated promising identification and verification performance.
Effective results were achieved across various experimental setups and standard handwriting databases (BFL, CVL).

Conclusions:

The proposed implicit shape codebook technique is effective for writer recognition from handwriting images.
This approach offers a viable solution for applications requiring accurate writer identification.