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

Applied Sciences

Verónica Aubin¹  Marco Mora²  José Naranjo-Torres² 

[1] Department of Engineering and Technological Research, Universidad Nacional de La Matanza, San Justo B1754JEC, Provincia de Buenos Aires, Argentina;Laboratory of Technological Research in Pattern Recognition, Faculty of Engineering Science, Universidad Católica del Maule, Talca 3480112, Maule, Chile;

关键词: writer identification;    off-line analysis;    simple graphemes;    convolutional neural networks;   

DOI  :  10.3390/app10227999

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown