Abstract
The writer’s identification/verification problem has traditionally been solved by analyzing complex biometric sources (text pages, paragraphs, words, signatures, etc.). This implies the need for pre-processing techniques, feature computation and construction of also complex classifiers. A group of simple graphemes (“ S ”, “ ∩ ”, “ C ”, “ ∼ ” and “ U ”) has been recently introduced in order to reduce the structural complexity of biometric sources. This paper proposes to analyze the images of simple graphemes by means of Convolutional Neural Networks. In particular, the AlexNet, VGG-16, VGG-19 and ResNet-18 models are considered in the learning transfer mode. The proposed approach has the advantage of directly processing the original images, without using an intermediate representation, and without computing specific descriptors. This allows to dramatically reduce the complexity of the simple grapheme processing chain and having a high hit-rate of writer identification performance.
Highlights
There are different biometric features that allow the verification or identification of people, among them is writing
In order to simplify the pipeline of simple graphemes processing, without to perform pre-processing, without to compute descriptors, and to achieve a high rate of writer identification accuracy, this paper proposes to analyze the image of the Simple Grapheme using Convolutional Neural Networks (CNN)
The first one consists of the rectified grapheme, which is the approach used in most articles that work with simple graphemes
Summary
There are different biometric features that allow the verification or identification of people, among them is writing. In order to simplify the pipeline of simple graphemes processing, without to perform pre-processing (working directly with the original image), without to compute descriptors, and to achieve a high rate of writer identification accuracy, this paper proposes to analyze the image of the Simple Grapheme using Convolutional Neural Networks (CNN) The advantages of this approach are as follows:. The idea of the ResNet models (ResNet-18/50/101), is to use residual blocks of direct access connections, with double or triple layer jumps where the input is not weighted and it is passed to a deeper layer In this work, this group of CNN networks is adopted because they present a good compromise between performance, structural complexity and training time.
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