Writer identification approach by holistic graphometric features using off-line handwritten words

Abstract

The biometric identification is an important topic with applications in different fields. Among the different modalities, based-handwriting biometric is a very useful and extended modality, and the most known one is the signature. The use of handwritten texts is researched presenting a biometric system for identifying writers from their handwritten words. A set of feature-based graphometric information has been extracted from off-line handwritten words to implement an automatic biometric approach. Given the handwritten nature of the information and its great variability, a feature selection based on principal component analysis and neural network classifier has been proposed. A fusion block based on neural networks has been added in order to reduce the effect of the data variability due to an increase and stabilization of the accuracy. A dataset composed of 100 writers have been used for the experiments. A holdout cross-validation was applied and the accuracy reached between 99.80% and 100%.

Introductory Chapter: The Fundamentals of Biometrics – A Comprehensive Exploration

“…One of the key strengths of biometrics lies in its ability to provide strong authentication while enhancing user convenience and experience [9]. Unlike passwords or PINs, which can be forgotten, stolen, or shared, biometric traits are inherently tied to the individual and cannot be easily compromised.…”

Writer Identification Using Handwritten Cursive Texts and Single Character Words

“…Previous studies [37][38][39][40] have focused on time or stroke features of handwriting. A writer recognition system for touch-screen mobile devices was proposed in Reference [37] for non-Latin languages with a large set of characters.…”

Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes

“…Most state-of-the-art works analyze complex text structures to extract features, such as full pages, text and paragraphs [2][3][4][5][6], words [7][8][9] and signatures [10][11][12]. Working with very complex sources in order to obtain a high verification ratio results in complexity throughout the entire processing sequence: developing sophisticated segmentation algorithms for the region of interest, complexity in the automatic computation of descriptors to represent the original data with low dimensionality and high execution times for the algorithms.…”